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, …
Algebraformer: A Neural Approach to Linear Systems
NeurIPS Workshop on Machine Learning and the Physical Sciences, 2025
Recent work in deep learning has opened new possibilities for solving classical algorithmic tasks using end-to-end learned models. In this work, we investigate the fundamental task of solving linear systems, particularly those that are ill-conditioned. Existing numerical methods for ill-conditioned systems often …