md-simulations Equiformer by Yi-Lun Liao (MIT) Molecular Simulations with Pretrained Neural Nets & Universal Pairwise Force Fields by Adil Kabylda (University of Luxembourg) NeuqIP by Simon Batzner from Harvard University. Path Integral SOC for Path Sampling (PIPS) by Lars Holdijk from Oxford University. pde Clifford-Steerable CNNs by Maksim Zhdanov (UvA, NL) 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) HAMLET: Graph Transformer Neural Operator for Partial Differential Equations by Andrey Bryutkin (MIT, USA) 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) Poseidon: Efficient Foundation Models for Partial Differential Equations by Maximilian Herde (ETH-Z, CH) 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) TENG: Time-Evolving Natural Gradient for Solving PDEs with Deep Neural Net by Zhuo Chen (IAIFI/MIT, USA) 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 Injecting Hamiltonian Architectural Bias into Deep Graph Networks for Long-Range Propagation by Simon Heilig (FAU, DE) and Alessio Gravina (Pisa, IT) Phenomenological Understanding of Neural Networks by Samuel Tovey from University of Stuttgart.
