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Data Analysis and Problem Solving Seminar
Data Analysis and Problem Solving Seminar
Physics-Informed Neural Networks: Software Tools, a DeepXDE Case Study, and Future Directions
Physics-Informed Neural Networks: Software Tools, a DeepXDE Case Study, and Future Directions
Organizer
Speaker
Time
Thursday, July 16, 2026 3:00 PM - 4:00 PM
Venue
A3-1-301
Online
Zoom 204 323 0165
(BIMSA)
Abstract
Physics-informed neural networks provide a data-driven approach to solving differential equations by incorporating physical laws, boundary conditions, and initial conditions into neural network training. This talk introduces the practical use of PINNs from the perspective of scientific software. We first demonstrate the complete PINN workflow in DeepXDE through the viscous Burgers equation, including problem definition, sampling, network construction, loss formulation, optimization, and accuracy evaluation. The predicted solution is compared with a reference solution to illustrate both the overall performance and the difficulty of resolving regions with sharp changes. We then review representative scientific machine-learning libraries, including DeepXDE, PhysicsNeMo, NeuroDiffEq, TorchPhysics, SciANN, NeuralPDE.jl, ADCME, TensorDiffEq, NeuralUQ, and DUE, highlighting their different capabilities and application scenarios. Finally, the talk discusses four major challenges that must be addressed before PINNs can become more reliable scientific computing tools: training stability, representation of multiscale and rapidly changing solutions, computational efficiency, and integration with traditional numerical methods.