IEEE

Federated Scientific Machine Learning for Approximating Functions and Solving Differential Equations With Data Heterogeneity

Abstract: By leveraging neural networks, the emerging field of scientific machine learning (SciML) offers novel approaches to address complex problems governed by partial differential equations (PDEs) ...
IEEE

Fast Solving Partial Differential Equations via Imitative Fourier Neural Operator

Abstract: Neural operators are a class of neural networks to learn mappings between infinite-dimensional function spaces, and recent studies have shown that using neural operators to solve partial ...
Yahoo Finance

LightSolver Announces Breakthrough in Physical Modeling on the LPU and New Roadmap for Optical, Analog PDE Solving

TEL AVIV, Israel, Sept. 16, 2025 (GLOBE NEWSWIRE) -- LightSolver, inventors of a new laser-based computing paradigm, today announced it has achieved a major technological breakthrough: the ability to ...
GitHub

Problem constructing a suitable NN architecture to solve partial differential equations with non-separable solutions

Have physics-informed neural networks been shown to solve partial differential equations whose solutions are non-separable functions? What suitable setups work?: deep/shallow NNs, what type of ...