Simplifying Transformer-Based U-Net Neural Physics Simulators
Advances in Neural Information Processing Systems (NeurIPS), 2026
U-Net-style architectures are widely adopted for modeling physical systems, as their multiscale structure enables efficient processing of high-resolution data while reflecting the hierarchical structure of many physical phenomena. The most successful U-Net-based neural physics simulators often combine convolutions, …
Neural-HSS: Hierarchical Semi-Separable Neural PDE Solver
International Conference on Machine Learning (ICML), 2026
Deep learning-based methods have shown remarkable effectiveness in solving PDEs, largely due to their ability to enable fast simulations once trained. However, despite the availability of high-performance computing infrastructure, many critical applications remain constrained by the substantial computational costs …
Ambient Physics: Training Neural PDE Solvers with Partial Observations
In many scientific contexts, obtaining complete observations of PDE coefficients and solutions proves expensive, hazardous, or unfeasible. Recent diffusion-based approaches can reconstruct fields from incomplete data, yet require fully observed examples during training. We present Ambient Physics, a framework enabling …
Test-Time Accuracy-Cost Control in Neural Simulators via Recurrent-Depth
International Conference on Learning Representations (ICLR), 2026
Accuracy-cost trade-offs are a fundamental aspect of scientific computing. Classical numerical methods inherently offer such a trade-off: increasing resolution, order, or precision typically yields more accurate solutions at higher computational cost. We introduce \textbf{Recurrent-Depth Simulator} (\textbf{RecurrSim}) …
Solaris: A Foundation Model for the Sun
NeurIPS Workshop on Foundation Models for Science, 2024
Foundation models have demonstrated remarkable success across various scientific domains, motivating our exploration of their potential in solar physics. In this paper, we present Solaris, the first foundation model for forecasting the Sun’s atmosphere. We leverage 13 years of full-disk, multi-wavelength solar imagery …
Mixture of Neural Operators: Incorporating Historical Information for Longer Rollouts
ICLR Workshop on AI4DifferentialEquations in Science, 2024
Traditional numerical solvers for time-dependent partial differential equations (PDEs) notoriously require high computational resources and necessitate recomputation when faced with new problem parameters. In recent years, neural surrogates have shown great potential to overcome these limitations. However, it has been …