From a5bc7e615d254de9e7e7a7c2b9a7a2ddfa86ded6 Mon Sep 17 00:00:00 2001
From: Timo Betcke <timo.betcke@gmail.com>
Date: Fri, 18 Oct 2024 12:08:37 +0100
Subject: [PATCH] Small fixes

---
 _quarto.yml                                   |  2 +-
 index.qmd                                     |  1 +
 phd_projects/entries/Benning_Hearing.qmd      |  2 +-
 phd_projects/entries/Benning_Paralift.qmd     |  2 +-
 .../entries/Berloff_Quasigeostrophic.qmd      |  2 +-
 .../entries/Beskos_bayesiancalibration.qmd    |  2 +-
 .../entries/BetckeM_inverseproblems.qmd       |  2 +-
 .../entries/Betcke_GPUarchitectures.qmd       |  2 +-
 phd_projects/entries/Boulle_Koopman.qmd       |  2 +-
 phd_projects/entries/Bravi_cancerimmune.qmd   |  2 +-
 .../entries/Briol_Bayesianinference.qmd       |  2 +-
 phd_projects/entries/Briol_Montecarlo.qmd     |  2 +-
 phd_projects/entries/Briol_simulation.qmd     |  2 +-
 .../entries/Burman_heterogeneouscoupling.qmd  |  2 +-
 .../entries/CotterKalise_Fieldgames.qmd       |  2 +-
 .../entries/Cotter_hybridmodelling.qmd        |  2 +-
 phd_projects/entries/Coveney_HemeLB.qmd       |  2 +-
 .../entries/Coveney_dynamicalsystems.qmd      |  2 +-
 phd_projects/entries/Cox_kwave.qmd            |  2 +-
 phd_projects/entries/Cox_neuralODE.qmd        |  2 +-
 .../entries/Deisenroth_dataassimilation.qmd   |  2 +-
 .../entries/Graham_Automatedbayesian.qmd      |  2 +-
 .../entries/Graham_multilevelbayesian.qmd     |  2 +-
 .../entries/Hewett_fractaldomains.qmd         |  2 +-
 .../entries/Jensen_unifiedformlanguage.qmd    |  2 +-
 phd_projects/entries/Karin_tissue.qmd         |  2 +-
 phd_projects/entries/Kovalchuk_healthcare.qmd |  2 +-
 .../entries/MisenerHam_DAEinterface.qmd       |  2 +-
 phd_projects/entries/Ni_StochasticPDE.qmd     |  9 ++--
 .../entries/Parpas_moleculardynamics.qmd      |  2 +-
 phd_projects/entries/Pavliotis_inference.qmd  |  2 +-
 .../entries/Pearce_bacterialbiofilms.qmd      |  2 +-
 phd_projects/entries/Salvi_Gillespie.qmd      |  2 +-
 phd_projects/entries/Tennyson_TROVE.qmd       |  2 +-
 .../entries/ThomasShahrezaei_inference.qmd    |  2 +-
 .../entries/Tobar_machinelearning.qmd         |  2 +-
 phd_projects/entries/burman_inverse.qmd       |  2 +-
 phd_projects/entries/cotter_parallel_time.qmd |  2 +-
 phd_projects/entries/gelat.qmd                |  2 +-
 phd_projects/entries/gutschow.qmd             | 47 +++++++++++++++++++
 phd_projects/entries/haqshenas.qmd            |  2 +-
 .../entries/lamb_deep_learning copy.qmd       | 26 ++++++++++
 phd_projects/entries/pavliotis_kalise.qmd     | 28 +++++++++++
 .../entries/salvi_backpropagation.qmd         |  2 +-
 .../entries/zhang_reinforcement_learning.qmd  |  2 +-
 45 files changed, 147 insertions(+), 44 deletions(-)
 create mode 100644 phd_projects/entries/gutschow.qmd
 create mode 100644 phd_projects/entries/lamb_deep_learning copy.qmd
 create mode 100644 phd_projects/entries/pavliotis_kalise.qmd

diff --git a/_quarto.yml b/_quarto.yml
index d68344a..7390b58 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -41,7 +41,7 @@ website:
 
 format:
   html:
-    theme: morph
+    theme: default
     css: styles.css
     toc: false
     smooth-scrolling: true
diff --git a/index.qmd b/index.qmd
index 40ae1aa..18f6a52 100644
--- a/index.qmd
+++ b/index.qmd
@@ -41,6 +41,7 @@ gallery-images:
 </div>
 </div>
 
+<br>
 
 
 <section id="gallery">
diff --git a/phd_projects/entries/Benning_Hearing.qmd b/phd_projects/entries/Benning_Hearing.qmd
index 5e9be71..c811a87 100644
--- a/phd_projects/entries/Benning_Hearing.qmd
+++ b/phd_projects/entries/Benning_Hearing.qmd
@@ -3,7 +3,7 @@ title: "Improving impaired hearing through sound reconstruction from neural acti
 institution: "UCL"
 department: "Computer Science"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Martin Benning"
 
 ---
diff --git a/phd_projects/entries/Benning_Paralift.qmd b/phd_projects/entries/Benning_Paralift.qmd
index 4af3c17..5310cce 100644
--- a/phd_projects/entries/Benning_Paralift.qmd
+++ b/phd_projects/entries/Benning_Paralift.qmd
@@ -3,7 +3,7 @@ title: "PARALIFT – A PARAllel framework for LIFTed training of neural networks
 institution: "UCL"
 department: "Computer Science"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Martin Benning"
 
 ---
diff --git a/phd_projects/entries/Berloff_Quasigeostrophic.qmd b/phd_projects/entries/Berloff_Quasigeostrophic.qmd
index f91e040..04aeb8a 100644
--- a/phd_projects/entries/Berloff_Quasigeostrophic.qmd
+++ b/phd_projects/entries/Berloff_Quasigeostrophic.qmd
@@ -3,7 +3,7 @@ title: "Developing Quasi-Geostrophic Coupled Ocean–Atmosphere Model"
 institution: "Imperial"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Pavel Berloff"
 
 ---
diff --git a/phd_projects/entries/Beskos_bayesiancalibration.qmd b/phd_projects/entries/Beskos_bayesiancalibration.qmd
index f4ffec6..584c5ad 100644
--- a/phd_projects/entries/Beskos_bayesiancalibration.qmd
+++ b/phd_projects/entries/Beskos_bayesiancalibration.qmd
@@ -3,7 +3,7 @@ title: "Advanced Computational Methods for Bayesian Calibration of High-Dimensio
 institution: "UCL"
 department: "Statistics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Alexandros Beskos"
 
 ---
diff --git a/phd_projects/entries/BetckeM_inverseproblems.qmd b/phd_projects/entries/BetckeM_inverseproblems.qmd
index 2ad3de3..a5a4ab6 100644
--- a/phd_projects/entries/BetckeM_inverseproblems.qmd
+++ b/phd_projects/entries/BetckeM_inverseproblems.qmd
@@ -3,7 +3,7 @@ title: "All-at-once deep learning methods for nonlinear PDE based inverse proble
 institution: "UCL"
 department: "Computer Science"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Marta Betcke"
 
 ---
diff --git a/phd_projects/entries/Betcke_GPUarchitectures.qmd b/phd_projects/entries/Betcke_GPUarchitectures.qmd
index aaaa1c0..adaff47 100644
--- a/phd_projects/entries/Betcke_GPUarchitectures.qmd
+++ b/phd_projects/entries/Betcke_GPUarchitectures.qmd
@@ -3,7 +3,7 @@ title: "Fast Multipole Methods on modern architectures"
 institution: "UCL"
 department: "Maths and Advanced Research Computing Centre"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Timo Betcke"
 
 ---
diff --git a/phd_projects/entries/Boulle_Koopman.qmd b/phd_projects/entries/Boulle_Koopman.qmd
index 86dd668..e665b44 100644
--- a/phd_projects/entries/Boulle_Koopman.qmd
+++ b/phd_projects/entries/Boulle_Koopman.qmd
@@ -3,7 +3,7 @@ title: "Koopman operator learning of nonlinear PDEs"
 institution: "Imperial"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr.Nicholas Boulle"
 
 ---
diff --git a/phd_projects/entries/Bravi_cancerimmune.qmd b/phd_projects/entries/Bravi_cancerimmune.qmd
index 5acc8ee..b375e2d 100644
--- a/phd_projects/entries/Bravi_cancerimmune.qmd
+++ b/phd_projects/entries/Bravi_cancerimmune.qmd
@@ -3,7 +3,7 @@ title: "A machine learning-informed computational model of cancer-immune interac
 institution: "Imperial"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr. Barbara Bravi"
 
 ---
diff --git a/phd_projects/entries/Briol_Bayesianinference.qmd b/phd_projects/entries/Briol_Bayesianinference.qmd
index aec4d18..b904047 100644
--- a/phd_projects/entries/Briol_Bayesianinference.qmd
+++ b/phd_projects/entries/Briol_Bayesianinference.qmd
@@ -3,7 +3,7 @@ title: "Robust Bayesian Inference at Scale"
 institution: "UCL"
 department: "Statistical Science"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr.Francois-Xavier Briol"
 
 ---
diff --git a/phd_projects/entries/Briol_Montecarlo.qmd b/phd_projects/entries/Briol_Montecarlo.qmd
index 01f6c27..03ba85c 100644
--- a/phd_projects/entries/Briol_Montecarlo.qmd
+++ b/phd_projects/entries/Briol_Montecarlo.qmd
@@ -3,7 +3,7 @@ title: "Transfer Learning for Monte Carlo"
 institution: "UCL"
 department: "Statistical Science"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr.Francois-Xavier Briol"
 
 ---
diff --git a/phd_projects/entries/Briol_simulation.qmd b/phd_projects/entries/Briol_simulation.qmd
index 0a57bcf..b726e34 100644
--- a/phd_projects/entries/Briol_simulation.qmd
+++ b/phd_projects/entries/Briol_simulation.qmd
@@ -3,7 +3,7 @@ title: "Simulation-based Inference for Expensive Scientific Simulators"
 institution: "UCL"
 department: "Statistical Science"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr.Francois-Xavier Briol"
 
 ---
diff --git a/phd_projects/entries/Burman_heterogeneouscoupling.qmd b/phd_projects/entries/Burman_heterogeneouscoupling.qmd
index d2f1ccf..fb40b1d 100644
--- a/phd_projects/entries/Burman_heterogeneouscoupling.qmd
+++ b/phd_projects/entries/Burman_heterogeneouscoupling.qmd
@@ -3,7 +3,7 @@ title: "A computational framework for heterogeneous coupling in large scale comp
 institution: "UCL"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Erik Burman"
 
 ---
diff --git a/phd_projects/entries/CotterKalise_Fieldgames.qmd b/phd_projects/entries/CotterKalise_Fieldgames.qmd
index a62efa5..affe876 100644
--- a/phd_projects/entries/CotterKalise_Fieldgames.qmd
+++ b/phd_projects/entries/CotterKalise_Fieldgames.qmd
@@ -3,7 +3,7 @@ title: "A Unified Solver for Optimal Transport, Schroedinger Bridges, and Variat
 institution: "Imperial"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Colin Cotter and Dr. Dante Kalise"
 
 ---
diff --git a/phd_projects/entries/Cotter_hybridmodelling.qmd b/phd_projects/entries/Cotter_hybridmodelling.qmd
index 69b9c80..185d701 100644
--- a/phd_projects/entries/Cotter_hybridmodelling.qmd
+++ b/phd_projects/entries/Cotter_hybridmodelling.qmd
@@ -3,7 +3,7 @@ title: "PDE-driven/data-driven hybrid modelling for data assimilation"
 institution: "Imperial"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Colin Cotter"
 
 ---
diff --git a/phd_projects/entries/Coveney_HemeLB.qmd b/phd_projects/entries/Coveney_HemeLB.qmd
index 5d5b3b2..ffa51b6 100644
--- a/phd_projects/entries/Coveney_HemeLB.qmd
+++ b/phd_projects/entries/Coveney_HemeLB.qmd
@@ -3,7 +3,7 @@ title: "HemeLB at the exascale and beyond: Implementing and studying whole human
 institution: "UCL"
 department: "Chemistry and Advanced Research Computing"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Peter V. Coveney"
 
 ---
diff --git a/phd_projects/entries/Coveney_dynamicalsystems.qmd b/phd_projects/entries/Coveney_dynamicalsystems.qmd
index a949c45..98d686e 100644
--- a/phd_projects/entries/Coveney_dynamicalsystems.qmd
+++ b/phd_projects/entries/Coveney_dynamicalsystems.qmd
@@ -3,7 +3,7 @@ title: "Quantifying and eliminating floating point pathologies in the simulation
 institution: "UCL"
 department: "Chemistry and Advanced Research Computing"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Peter V. Coveney"
 
 ---
diff --git a/phd_projects/entries/Cox_kwave.qmd b/phd_projects/entries/Cox_kwave.qmd
index 2c18032..2dfae2c 100644
--- a/phd_projects/entries/Cox_kwave.qmd
+++ b/phd_projects/entries/Cox_kwave.qmd
@@ -3,7 +3,7 @@ title: "Grid-independent pseudospectral models of broadband acoustic wave propag
 institution: "UCL"
 department: "Medical Physics and Biomedical Engineering"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Ben Cox"
 
 ---
diff --git a/phd_projects/entries/Cox_neuralODE.qmd b/phd_projects/entries/Cox_neuralODE.qmd
index 4b8e16d..04b244d 100644
--- a/phd_projects/entries/Cox_neuralODE.qmd
+++ b/phd_projects/entries/Cox_neuralODE.qmd
@@ -3,7 +3,7 @@ title: "Time Reversal Imaging and Learned Physics with Neural ODEs"
 institution: "UCL"
 department: "Medical Physics and Biomedical Engineering"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Ben Cox"
 
 ---
diff --git a/phd_projects/entries/Deisenroth_dataassimilation.qmd b/phd_projects/entries/Deisenroth_dataassimilation.qmd
index ff3ce7c..3bc3586 100644
--- a/phd_projects/entries/Deisenroth_dataassimilation.qmd
+++ b/phd_projects/entries/Deisenroth_dataassimilation.qmd
@@ -3,7 +3,7 @@ title: "Machine Learning for Low-Cost Data Assimilation"
 institution: "UCL"
 department: "Computer Science"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Marc Deisenroth"
 
 ---
diff --git a/phd_projects/entries/Graham_Automatedbayesian.qmd b/phd_projects/entries/Graham_Automatedbayesian.qmd
index fef420e..d81684e 100644
--- a/phd_projects/entries/Graham_Automatedbayesian.qmd
+++ b/phd_projects/entries/Graham_Automatedbayesian.qmd
@@ -3,7 +3,7 @@ title: "Accelerated multi-level Bayesian calibration of expensive simulations on
 institution: "UCL"
 department: "Advanced Research Computing Centre"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr Matt Graham"
 
 ---
diff --git a/phd_projects/entries/Graham_multilevelbayesian.qmd b/phd_projects/entries/Graham_multilevelbayesian.qmd
index 1618abb..dd19461 100644
--- a/phd_projects/entries/Graham_multilevelbayesian.qmd
+++ b/phd_projects/entries/Graham_multilevelbayesian.qmd
@@ -3,7 +3,7 @@ title: "Automated Bayesian inference in stochastic differential equation models"
 institution: "UCL"
 department: "Advanced Research Computing Centre"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr Matt Graham"
 
 ---
diff --git a/phd_projects/entries/Hewett_fractaldomains.qmd b/phd_projects/entries/Hewett_fractaldomains.qmd
index 35106fb..22aa640 100644
--- a/phd_projects/entries/Hewett_fractaldomains.qmd
+++ b/phd_projects/entries/Hewett_fractaldomains.qmd
@@ -3,7 +3,7 @@ title: "Numerical methods for PDEs in fractal domains"
 institution: "UCL"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof David Hewett"
 
 ---
diff --git a/phd_projects/entries/Jensen_unifiedformlanguage.qmd b/phd_projects/entries/Jensen_unifiedformlanguage.qmd
index aaa560f..65a242b 100644
--- a/phd_projects/entries/Jensen_unifiedformlanguage.qmd
+++ b/phd_projects/entries/Jensen_unifiedformlanguage.qmd
@@ -3,7 +3,7 @@ title: "A Unified Form Language for PDEs in non-divergence form"
 institution: "UCL"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr Max Jensen"
 
 ---
diff --git a/phd_projects/entries/Karin_tissue.qmd b/phd_projects/entries/Karin_tissue.qmd
index d5dfd95..edc0038 100644
--- a/phd_projects/entries/Karin_tissue.qmd
+++ b/phd_projects/entries/Karin_tissue.qmd
@@ -3,7 +3,7 @@ title: "Agent-based simulations of tissue dynamics"
 institution: "Imperial"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr.Omer Karin"
 
 ---
diff --git a/phd_projects/entries/Kovalchuk_healthcare.qmd b/phd_projects/entries/Kovalchuk_healthcare.qmd
index 6a804f9..6745098 100644
--- a/phd_projects/entries/Kovalchuk_healthcare.qmd
+++ b/phd_projects/entries/Kovalchuk_healthcare.qmd
@@ -3,7 +3,7 @@ title: "Drift detection in graph streams and its applications in healthcare"
 institution: "UCL"
 department: "Computer Science and Advanced Research Computing Centre"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr. Yevgeniya Kovalchuk"
 
 ---
diff --git a/phd_projects/entries/MisenerHam_DAEinterface.qmd b/phd_projects/entries/MisenerHam_DAEinterface.qmd
index 0c1ae85..7c6a890 100644
--- a/phd_projects/entries/MisenerHam_DAEinterface.qmd
+++ b/phd_projects/entries/MisenerHam_DAEinterface.qmd
@@ -3,7 +3,7 @@ title: "Algorithms and software for differentiable programming at the ML/PDE/DAE
 institution: "Imperial"
 department: "Computing and Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Ruth Misener and Prof David Ham"
 
 ---
diff --git a/phd_projects/entries/Ni_StochasticPDE.qmd b/phd_projects/entries/Ni_StochasticPDE.qmd
index 446d689..b33241b 100644
--- a/phd_projects/entries/Ni_StochasticPDE.qmd
+++ b/phd_projects/entries/Ni_StochasticPDE.qmd
@@ -3,7 +3,7 @@ title: "Accelerating parabolic Stochastic PDE solver via a weak adversarial netw
 institution: "UCL"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Hao Ni"
 
 ---
@@ -13,7 +13,7 @@ advisor: "Prof Hao Ni"
 
 
 Stochastic partial differential equations (SPDEs) are power mathematical tools to model random spatiotemporal dynamics, with broader applications ranging from weather forecasting to fluid dynamics.  Machine learning (ML)-based approaches are emerging as it can be used to effectively handle the curse of dimensionality of traditional numerical methods. It can lead to the high-performing SPDE solver with significant computational acceleration. Moreover, the generalization of physics- informed neural networks from PDEs to SPDEs may provide a novel family of neural networks for analysing noisy spatiotemporal data, which can be used to discover the hidden physics law and predict the future data evolution. 
-Most of ML algorithms for learning SPDEs fail into the supervised learning category, where the input and output pairs are “observable data” consisted of driving noise and the corresponding solution trajectories. These data are simulated by high quality numerical solver. One important aspect of improving ML algorithms is to design neural networks to better approximate the SPDE solution map [1 – 3]. Among them, our proposed Deep latent regularity network incorporates Regularity structure, a groundbreaking work on SPDEs by the Fields medallist Martin Hairer, to design neural networks. This model demonstrates superior performance in terms of accuracy and inferences time on various SPDEs such as the stochastic 2D Navier-Stokes equation. 
+Most of ML algorithms for learning SPDEs fall into the supervised learning category, where the input and output pairs are “observable data” consisted of driving noise and the corresponding solution trajectories. These data are simulated by high quality numerical solver. One important aspect of improving ML algorithms is to design neural networks to better approximate the SPDE solution map [1 – 3]. Among them, our proposed Deep latent regularity network incorporates Regularity structure, a groundbreaking work on SPDEs by the Fields medallist Martin Hairer, to design neural networks. This model demonstrates superior performance in terms of accuracy and inferences time on various SPDEs such as the stochastic 2D Navier-Stokes equation. 
 
 
 ### Main objectives of the project
@@ -40,7 +40,8 @@ Reference
 
 [3].	Gong, S., Hu, P., Meng, Q., Wang, Y., Zhu, R., Chen, B., Ma, Z., Ni, H. and Liu, T.Y., 2023, June. Deep latent regularity network for modeling stochastic partial differential equations. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 6, pp. 7740-7747).
 
-[4].	[1] Zang, Y., Bao, G., Ye, X. and Zhou, H., 2020. Weak adversarial networks for high-dimensional partial differential equations. Journal of Computational Physics, 411, p.109409.
+[4].	Zang, Y., Bao, G., Ye, X. and Zhou, H., 2020. Weak adversarial networks for high-dimensional partial differential equations. Journal of Computational Physics, 411, p.109409.
+
+[5].	Oliva, P.V., Wu, Y., He, C. and Ni, H., 2022. Towards fast weak adversarial training to solve high dimensional parabolic partial differential equations using XNODE-WAN. Journal of Computational Physics, 463, p.111233.
 
-[5].	[2] Oliva, P.V., Wu, Y., He, C. and Ni, H., 2022. Towards fast weak adversarial training to solve high dimensional parabolic partial differential equations using XNODE-WAN. Journal of Computational Physics, 463, p.111233.
 [6].	Chevyrev, Ilya, Andris Gerasimovics, and Hendrik Weber. "Feature engineering with regularity structures." arXiv preprint arXiv:2108.05879 (2021).
\ No newline at end of file
diff --git a/phd_projects/entries/Parpas_moleculardynamics.qmd b/phd_projects/entries/Parpas_moleculardynamics.qmd
index 8b76320..3365e73 100644
--- a/phd_projects/entries/Parpas_moleculardynamics.qmd
+++ b/phd_projects/entries/Parpas_moleculardynamics.qmd
@@ -3,7 +3,7 @@ title: "Efficient Computation of Transition States in Molecular Dynamics"
 institution: "Imperial"
 department: "Computing"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr. Panos Parpas"
 
 ---
diff --git a/phd_projects/entries/Pavliotis_inference.qmd b/phd_projects/entries/Pavliotis_inference.qmd
index cfaf478..9b4e5c1 100644
--- a/phd_projects/entries/Pavliotis_inference.qmd
+++ b/phd_projects/entries/Pavliotis_inference.qmd
@@ -3,7 +3,7 @@ title: "Inference and inverse problems for stochastic interacting particle syste
 institution: "Imperial"
 department: "Mathemetics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Greg A. Pavliotis"
 
 ---
diff --git a/phd_projects/entries/Pearce_bacterialbiofilms.qmd b/phd_projects/entries/Pearce_bacterialbiofilms.qmd
index 669b167..d75b317 100644
--- a/phd_projects/entries/Pearce_bacterialbiofilms.qmd
+++ b/phd_projects/entries/Pearce_bacterialbiofilms.qmd
@@ -3,7 +3,7 @@ title: "Data-driven multi-scale modelling of bacterial biofilms"
 institution: "UCL"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr. Philip Pearce"
 
 ---
diff --git a/phd_projects/entries/Salvi_Gillespie.qmd b/phd_projects/entries/Salvi_Gillespie.qmd
index 7fae078..4a99ede 100644
--- a/phd_projects/entries/Salvi_Gillespie.qmd
+++ b/phd_projects/entries/Salvi_Gillespie.qmd
@@ -3,7 +3,7 @@ title: "A differentiable Gillespie algorithm for exact gradients through discret
 institution: "Imperial"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr.Cristopher Salvi"
 
 ---
diff --git a/phd_projects/entries/Tennyson_TROVE.qmd b/phd_projects/entries/Tennyson_TROVE.qmd
index 0a7e9ab..0cdc3a2 100644
--- a/phd_projects/entries/Tennyson_TROVE.qmd
+++ b/phd_projects/entries/Tennyson_TROVE.qmd
@@ -3,7 +3,7 @@ title: "Re-griding the TROVE nuclear motion program"
 institution: "UCL"
 department: "Physics and Astronomy"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Jonathan Tennyson"
 
 ---
diff --git a/phd_projects/entries/ThomasShahrezaei_inference.qmd b/phd_projects/entries/ThomasShahrezaei_inference.qmd
index 55d6a92..ea97429 100644
--- a/phd_projects/entries/ThomasShahrezaei_inference.qmd
+++ b/phd_projects/entries/ThomasShahrezaei_inference.qmd
@@ -3,7 +3,7 @@ title: "Inference of gene regulatory networks from single cell data"
 institution: "Imperial"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Prof Vahid Shahrezaei and Dr. Philipp Thomas "
 
 ---
diff --git a/phd_projects/entries/Tobar_machinelearning.qmd b/phd_projects/entries/Tobar_machinelearning.qmd
index d621240..90b5716 100644
--- a/phd_projects/entries/Tobar_machinelearning.qmd
+++ b/phd_projects/entries/Tobar_machinelearning.qmd
@@ -3,7 +3,7 @@ title: "Optimal transport for probabilistic machine learning"
 institution: "Imperial"
 department: "Mathematics"
 author: "Laura Beer"
-date: "02/10/2024"
+date: "10/02/2024"
 advisor: "Dr.Felipe Tobar"
 
 ---
diff --git a/phd_projects/entries/burman_inverse.qmd b/phd_projects/entries/burman_inverse.qmd
index 5fdcc36..0c34925 100644
--- a/phd_projects/entries/burman_inverse.qmd
+++ b/phd_projects/entries/burman_inverse.qmd
@@ -3,7 +3,7 @@ title: "Computational solution of inverse problems using large datasets of low r
 institution: "UCL"
 department: "Mathematics"
 author: "Timo Betcke"
-date: "30/09/2024"
+date: "09/30/2024"
 advisor: "Prof Erik Burman"
 
 ---
diff --git a/phd_projects/entries/cotter_parallel_time.qmd b/phd_projects/entries/cotter_parallel_time.qmd
index 24cbb84..4771900 100644
--- a/phd_projects/entries/cotter_parallel_time.qmd
+++ b/phd_projects/entries/cotter_parallel_time.qmd
@@ -3,7 +3,7 @@ title: "Data-driven preconditioning for parallel-in-time PDE solvers"
 institution: "Imperial"
 department: "Mathematics"
 author: "Laura Beer"
-date: "11/10/2024"
+date: "10/11/2024"
 advisor: "Prof Colin Cotter"
 
 ---
diff --git a/phd_projects/entries/gelat.qmd b/phd_projects/entries/gelat.qmd
index d9264cd..8722a15 100644
--- a/phd_projects/entries/gelat.qmd
+++ b/phd_projects/entries/gelat.qmd
@@ -3,7 +3,7 @@ title: "Large-scale high-performance solver for therapeutic ultrasound applicati
 institution: "UCL"
 department: "UCL Division of Surgery and Interventional Science"
 author: "Timo Betcke"
-date: "11/10/2024"
+date: "10/11/2024"
 advisor: "Dr Pierre Gélat"
 
 ---
diff --git a/phd_projects/entries/gutschow.qmd b/phd_projects/entries/gutschow.qmd
new file mode 100644
index 0000000..778a1aa
--- /dev/null
+++ b/phd_projects/entries/gutschow.qmd
@@ -0,0 +1,47 @@
+---
+title: UCL - Scalable Collider Data Analysis with Optimised Predictions and Machine-Learning Anomaly Detection
+institution: UCL
+department: "Advanced Research Computing"
+author: "Timo Betcke"
+date: 10/18/2024
+advisor: Christian Gutschow
+---
+
+## Existing background work
+
+The Large Hadron Collider (LHC) has generated an immense amount of data, which,
+when combined with advanced Standard Model (SM) predictions, presents new opportunities
+for discovering physics beyond the SM. Current analysis frameworks, such as [Rivet](https://rivet.hepforge.org/),
+allow for data-theory comparisons, but incorporating higher-order calculations (NLO, NNLO)
+into these comparisons remains challenging. Additionally, there is potential to enhance
+traditional statistical analyses with machine learning-based anomaly detection to identify
+mismodelling or uncover hints of new physics. This project aims to improve both the affordability
+and accuracy of SM calculations and apply them to large-scale metadata analysis of collider data.
+
+
+## Main objectives of the project
+
+1.	**Optimising SM Calculations:** The project seeks to make state-of-the-art SM predictions
+    computationally affordable and accessible for large-scale collider data analysis. This will
+    be achieved through performance profiling of existing tools and integrating higher-order
+    calculations into Monte Carlo (MC) particle-level predictions using reweighting techniques.
+2.	**Comprehensive Metadata Analysis:** The student will perform large-scale metadata analysis
+    using Rivet, comparing experimental data from LHC experiments like ATLAS and CMS with optimised
+    SM predictions. *P*-value distributions will be generated to quantify the level of agreement between
+    theory and data across multiple observables.
+3.	**Anomaly Detection Framework:** Machine learning algorithms will be applied to *p*-value distributions
+    to detect anomalies in data-theory comparisons. This will enhance sensitivity in identifying potential
+    Monte Carlo mismodelling or uncovering signals of new physics.
+
+
+## Details of Software/Data Deliverables
+
+1.	**Performance-Optimised Prediction Tools:** The student will profile existing SM tools (e.g. Sherpa, Herwig)
+    to identify and address computational bottlenecks, optimising them for large-scale collider data comparisons.
+2.	**Integration of Higher-Order Calculations:** Incorporation of NLO and NNLO corrections into MC particle-level
+    predictions through reweighting techniques, making these sophisticated calculations accessible for widespread use.
+3.	**Data-Theory Comparison Framework:** A Rivet-based framework for generating detailed *p*-value distributions,
+    which will provide a robust statistical foundation for comparing experimental data with SM predictions.
+4.	**Machine Learning-Based Anomaly Detection:** Development of a machine learning-enhanced system to detect
+    inconsistencies in data-theory comparisons, using unsupervised learning techniques to identify potential
+    new physics signals or MC mismodelling.
\ No newline at end of file
diff --git a/phd_projects/entries/haqshenas.qmd b/phd_projects/entries/haqshenas.qmd
index da65687..eec93eb 100644
--- a/phd_projects/entries/haqshenas.qmd
+++ b/phd_projects/entries/haqshenas.qmd
@@ -3,7 +3,7 @@ title: "High-performance solver for ultrasound propagation in mixed domains with
 institution: "UCL"
 department: "Mechanical Engineering"
 author: "Timo Betcke"
-date: "11/10/2024"
+date: "10/11/2024"
 advisor: "Dr Reza Haqshenas"
 
 ---
diff --git a/phd_projects/entries/lamb_deep_learning copy.qmd b/phd_projects/entries/lamb_deep_learning copy.qmd
new file mode 100644
index 0000000..dadda1c
--- /dev/null
+++ b/phd_projects/entries/lamb_deep_learning copy.qmd	
@@ -0,0 +1,26 @@
+---
+title: "Deep learning with symmetry"
+institution: "Imperial"
+department: "Mathematics"
+author: "Timo Betcke"
+date: "10/18/2024"
+advisor: "Prof Jeroen Lamb"
+---
+
+### Project Description
+
+This project will study the equivariance of deep learning. The large majority of equivariant deep learning approaches is ad-hoc, and the direction of the PhD project is to exploit the algebraic theory of invariant and equivariant functions in a systematic way to develop a versatile and general approach to learning in the presence of symmetry. A few important tasks that can be achieved with such general theory are learning symmetry group orbits (e.g. recognizing objects, irrespective of their position or orientation), learning symmetry coordinates (e.g. the position and orientation of objects), and learning symmetries and near-symmetries (invariances and near-invariances).
+
+### Existing background work
+
+Equivariant deep learning is a subfield of deep learning that deals explicitly with symmetry in datasets and models. One of the most well-known and successful example is Convolutional Neural Networks (CNNs), used for the translation invariant machine learning of images. Equivariance is considered by many leading machine learners  as one of the essential ingredients, explaining the remarkable leap in efficiency of deep learning models [1]. It is also an important ingredient in physics informed modelling. Our group has a long track record of understanding symmetry in dynamical systems and we are now translating this to machine learning. 
+
+[1] M. Bronstein et al. Geometric deep learning. [https://geometricdeeplearning.com](https://geometricdeeplearning.com/)
+
+### Main objectives of the project
+
+The project will develop a systematic approach to develop algorithms that merge (computer) algebra with optimisation strategies in deep learning settings. There are many areas of applications where equivariant deep learning is the intrinsic objective. The PhD project aims to develop algorithms and software to demonstrate the effectiveness of the mathematical methodology in a concrete applied setting.
+
+### Details of Software/Data Deliverables
+
+The project will provide open source software enabling the implementation of equivariant learning strategies compatible with widely used deep learning frameworks.
diff --git a/phd_projects/entries/pavliotis_kalise.qmd b/phd_projects/entries/pavliotis_kalise.qmd
new file mode 100644
index 0000000..13f2237
--- /dev/null
+++ b/phd_projects/entries/pavliotis_kalise.qmd
@@ -0,0 +1,28 @@
+---
+title: "Optimal control methods for agent-based models"
+institution: "Imperial"
+department: "Mathematics"
+author: "Timo Betcke"
+date: "18/10/2024"
+advisor: "Prof Greg Pavliotis and Dr Dante Kalise"
+---
+
+## Project Description
+
+### Existing background work
+
+Greg Pavliotis is professor of Applied Mathematics at the department of Mathematics at Imperial College. His main research interests include statistical mechanics, multiscale systems, inference for stochastic processes and data-driven methods in applied mathematics. He has studied phase transitions for agent-based models (ABM) and interacting particle systems (IPS) and he has developed efficient inference methodologies for IPS and their mean field limit. He has also developed data-driven approaches to the study of ABMs.
+
+Dante Kalise is Reader in Optimisation and Control at the Department of Mathematics at Imperial College. His main research interests involve scientific computation and machine learning, optimal control, high-dimensional approximation and agent-based models across scales. He has developed optimal control methods for  high-dimensional problems which constitute the basis of this project, and developed applications in swarm robotics, pedestrian motion, and global optimization.
+
+### Main objectives of the project
+
+•	Agent-based models (ABM), often described in terms of systems of interacting diffusions (multidimensional stochastic differential equations) arise in many applications, including mathematical biology, e.g. models for chemotaxis, epidemiology,  and the social sciences, for example in models for opinion formation, pedestrian dynamics and macroeconomics. Such systems often exhibit interesting collective behaviour as a result of the interaction between agents. Several different macroscopic configurations of the ABM are possible, for different choices of the parameters in the system, such as the interaction strength. The transition between different macroscopic configurations can have dramatic effects on the behaviour of the ABM. The goal of the proposed project is to develop efficient control methodologies for steering the ABM dynamics towards desired macroscopic configurations. Different approaches, such as optimal control for PDEs and data driven approaches based on model predictive control will be developed. 
+
+### Details of Software/Data Deliverables
+
+Software deliverables will include:
+
+-	An ABM simulator for trajectory generation. To date, there’s no universal standard/benchmarks for ABM simulation, despite a generic structure based on the interaction forces in the system. We will develop a trajectory simulator which is fundamental for the study of data-driven methods and for optimisation and control purposes. Such simulator will provide sufficient freedom for prescribing interaction forces, thus being useful for different applications including opinion dynamics, swarm robotics, and pedestrian motion, among others.  The simulator will be constructed using state-of-the-art methods for accurate and fast approximation of large-scale SDEs.
+
+-	An optimal control toolbox for ABM. There are several optimal control solvers readily available for traditional control engineering applications (robotics, power electronics), however, none of them scale to high-dimensional settings which are natural in ABMs. Moreover, they do not exploit model structure and eventually resort to treating the optimal control problem as a large-scale, model-free, nonlinear optimization problem. Instead, we will develop a toolbox for control of ABMs based on adjoint calculus, making extensive use of the particular model structure of ABMs and circumventing calls to external nonlinear optimization solvers.
diff --git a/phd_projects/entries/salvi_backpropagation.qmd b/phd_projects/entries/salvi_backpropagation.qmd
index ce76582..95adbeb 100644
--- a/phd_projects/entries/salvi_backpropagation.qmd
+++ b/phd_projects/entries/salvi_backpropagation.qmd
@@ -3,7 +3,7 @@ title: "Backpropagation through rough differential equations"
 institution: "Imperial"
 department: "Mathematics & Imperial-X"
 author: "Timo Betcke"
-date: "11/10/2024"
+date: "10/11/2024"
 advisor: "Dr Christopher Salvi"
 
 ---
diff --git a/phd_projects/entries/zhang_reinforcement_learning.qmd b/phd_projects/entries/zhang_reinforcement_learning.qmd
index 500dea5..3c6f31d 100644
--- a/phd_projects/entries/zhang_reinforcement_learning.qmd
+++ b/phd_projects/entries/zhang_reinforcement_learning.qmd
@@ -3,7 +3,7 @@ title: "Optimization methods for high-dimensional reinforcement learning"
 institution: "Imperial"
 department: "Mathematics"
 author: "Timo Betcke"
-date: "11/10/2024"
+date: "10/11/2024"
 advisor: "Dr Yufei Zhang"
 
 ---