md-simulations Equiformer by Yi-Lun Liao (MIT) NeuqIP by Simon Batzner from Harvard University. Path Integral SOC for Path Sampling (PIPS) by Lars Holdijk from Oxford University. pde Climate Forecasting with Physics-Informed Neural ODEs by Yogesh Verma (Aalto University) Convolutional Neural Operators for Solving PDEs by Dr. Emmanuel de Bézenac (ETH Zurich) Implicit Neural Representation for PDEs by Louis Serrano (Sorbonne University - Paris) Data-driven Correction of Coarse Grid CFD Simulations by Anna Kiener (German Aerospace Center (DLR)) Continuous PDE Dynamics Forecasting with Neural Fields by Yuan Yin (Sorbonne Université) and Matthieu Kirchmeyer(Criteo AI Lab & Sorbonne Université) Neural Fields for PDEs by Dr. Peter Yichen Chen (MIT), Honglin Chen and Rundi Wu (Columbia). Machine Learning for CFD by Gideon Dresdner (ETH Zurich) PDE-Refiner by Phillip Lippe (MSR AI4Science/UvA) SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations by Xuan Zhang and Jacob Helwig (TAMU, USA) SSL for PDE by Quentin Garrido (Meta AI - FAIR/LIGM) How Temporal Unrolling Supports Neural Physics Simulators by Björn List (Technical University Munich) ml-chemistry DL for Density Functional Theory by Dr. Tianbo Li from SEA AI Lab, Singapura. ML for Chemistry by Dr. Marwin Segler from Microsoft Research Open Catalyst Project by Dr. Muhammed Shuaibi from FAIR, Meta AI. ml-materials AI for Materials Science by Dr. Lars Kotthoff (University of Wyoming) transfer-learning Cross-Modal Fine-Tuning by Junhong Shen from Carnegie Mellon University simulations Compositional Generative Inverse Design by Dr. Tailin Wu (Westlake University) Differentiable Physics by Benjamin Holzschuh from TU Munich physics neural-nets Phenomenological Understanding of Neural Networks by Samuel Tovey from University of Stuttgart.
