4 papers
Hard-constrained Physics-informed Neural Networks for Interface Problems
Seung Whan Chung, Stephen T. Castonguay, Sumanta Roy +3
Physics-informed neural networks (PINNs) have emerged as a flexible framework for solving partial differential equations, but their performance on interface problems remains challe…
DeepONet: A Discontinuity Capturing Neural Operator
Sumanta Roy, Stephen T. Castonguay, Pratanu Roy +1
We present DeepONet, a physics-informed neural operator designed to learn mappings between function spaces that may contain discontinuities or exhibit non-smooth behavior. Clas…
A Hereditary Integral, Transient Network Approach to Modeling Permanent Set and Viscoelastic Response in Polymers
Stephen T. Castonguay, Joshua B. Fernandes, Michael A. Puso +1
An efficient numerical framework is presented for modeling viscoelasticity and permanent set of polymers. It is based on the hereditary integral form of transient network theory, i…
Exact Enforcement of Temporal Continuity in Sequential Physics-Informed Neural Networks
Pratanu Roy, Stephen Castonguay
The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Inf…