From Numerical Simulators of PDEs to Neural Emulators and Back
cs.LG
Submitted: 2026-08-25
Updated: 2026-08-25
Code: https://github.com/NVIDIA/neuraloperator
Project page: https://tum-pbs.github.io/apebench
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics
- DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics
- The Elements of Differentiable Programming
- From Legacy Fortran to Portable Kokkos: An Autonomous Agentic AI Workflow
- Walrus: A Cross-Domain Foundation Model for Continuum Dynamics
- On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods
- Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation
- GraphCast: Learning skillful medium-range global weather forecasting
- Differentiability in Unrolled Training of Neural Physics Simulators on Transient Dynamics
- CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics
- Learned Coarse Models for Efficient Turbulence Simulation
- Explaining and Harnessing Adversarial Examples
- Adam: A Method for Stochastic Optimization
- Physics-Informed Neural Operator for Learning Partial Differential Equations
- What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning
- Multi-Scale Context Aggregation by Dilated Convolutions
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks