arXiv Machine Learning June 19, 2026 By Zhiwen Yu, Derong Yang, Liujian Zhang, Kaixiang Yang, Peilin Zhan, Jianmin Lv, Jane You, C. L. Philip Chen

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System

Read the original on arXiv Machine Learning →

arXiv:2606. 19754v1 Announce Type: new Abstract: Partial differential equations (PDEs) play a central role in modeling complex physical, biological, and engineering systems.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Physics-Informed Broad Learning System: An Efficient Backpropagation-Free Framework for Solving Partial Differential Equations

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Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing

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Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks

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Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations

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