AI&PDE: ICLR 2026 Workshop on AI and Partial Differential Equations

Eduardo Soares, Daniel Yukimura, Nara Bobko, Arthur Bizzi, Siddhartha Mishra, Elisa Serioli, Ana Paula Oliveira Muller

Keywords: AI for Science, Partial Differential Equations, Neural Operators, Physics-Informed Neural Networks, Foundation Models, Scientific Machine Learning, Multiphysics Modeling, Operator Learning, Benchmark Datasets, Stability and Generalization, Uncertainty Quantification, Multimodal Representations

Abstract:

Partial Differential Equations (PDEs) are foundational to modeling complex phenomena across the natural sciences and engineering, from fluid dynamics and quantum systems to climate modeling and materials science. Despite their ubiquity, solving PDEs remains computationally intensive, especially in high-dimensional, multi-physics, and uncertain regimes. Recent advances in machine learning—such as neural operators, physics-informed networks, and foundation models—offer transformative potential to accelerate and generalize PDE solutions. However, realizing this promise requires addressing critical challenges in representation, stability, generalization, and benchmarking.

The AI\&PDE-ICLR-2026 workshop will convene researchers from machine learning, applied mathematics, physics, and engineering to explore the future of AI-driven PDE modeling. We aim to (1) define the roadmap toward foundation models for PDEs, (2) investigate next-generation representations and architectures, and (3) foster a globally inclusive community. The program will feature invited talks, contributed papers, and themed tracks, including a full papers track for mature research and a tiny papers track for emerging ideas. By bridging disciplines and promoting open benchmarks and datasets, AI\&PDE-ICLR-2026 will catalyze progress toward scalable, general-purpose AI solvers for PDEs.

Submission Number: 84