Symplectic Neural Flows for Modeling and Discovery
https://arxiv.org/abs/2412.16787
Signal or Noise?
https://yang-ai-lab.github.io/sensor-gen/
Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach
https://arxiv.org/pdf/2509.24627
Scaling Nonlinear Optimization: Many Problems One GPU
https://arxiv.org/abs/2606.26341
NeuralGCM is a hybrid ML + physics global circulation model that pairs a differentiable spectral dynamical core with learned physics to forecast weather and run climate-scale simulations
https://dsip-fbk.github.io/neuralgcm-torch/intro.html#
Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
https://safe-autonomous-systems.github.io/fluidgym/
eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures
https://e-flesh.com/
InCoder-32B: Industrial Code Foundation Model
https://github.com/CSJianYang/Industrial-Coder
AI for Industry Challenge Toolkit
https://github.com/intrinsic-dev/aic
Electromagnetic FDTD Simulations in JAX
https://github.com/ymahlau/fdtdx
Walrus: A Cross-domain Foundation Model for Continuum Dynamics
https://github.com/PolymathicAI/walrus
Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
https://github.com/safe-autonomous-systems/fluidgym
Noether Framework
https://github.com/Emmi-AI/noether
Gym Electric Motor (GEM): An OpenAI Gym Environment for Electric Motors
https://github.com/upb-lea/gym-electric-motor <\br>
Physics-Informed Graph Neural Networks for Predicting Deformation in Disordered Fibrous Materials
Article link copied!
https://pubs.acs.org/doi/10.1021/acs.nanolett.5c04895
Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework
https://arxiv.org/abs/2511.03179
A Foundation Policy for Quadrotor Control
https://github.com/rl-tools/raptor
Two Roads to Koopman Operator Theory for Control
https://arxiv.org/pdf/2510.15166
Build Arena - The First Physics-Aligned Interactive Benchmark for Language-Driven Engineering Construction
https://github.com/AI4Science-WestlakeU/BuildArena
Koopman operator identification library in Python, compatible with scikit-learn
https://github.com/decargroup/pykoop
Introduction to reinforcement learning and control theory
https://www2.compute.dtu.dk/courses/02465/index.html
Generative AI for fast and accurate Statistical Computation of Fluids
https://arxiv.org/abs/2409.18359
Discovery of Unstable Singularities
https://arxiv.org/abs/2509.14185
An overview of Koopman-based control: From error bounds to closed-loop guarantees
https://arxiv.org/abs/2509.02839
Topological classification of driven-dissipative nonlinear systems
https://www.science.org/doi/10.1126/sciadv.adt9311
Stable Sparse Operator Inference for Nonlinear Structural Dynamics
https://arxiv.org/abs/2407.21672
Hamiltonian Theory and Computation of Optimal Probability Density Control in High Dimensions
https://arxiv.org/abs/2505.18362
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems
https://arxiv.org/abs/2502.11057
Score Matching Diffusion Based Feedback Control and Planning of Nonlinear Systems
https://arxiv.org/abs/2504.09836
EngiBench: A standard API, Open-Source Datasets and Benchmarks for Engineering Design
https://engibench.ethz.ch/