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cs.LG2025
Physics-Informed Neural Network Frameworks for the Analysis of Engineering and Biological Dynamical Systems Governed by Ordinary Differential Equations
Tyrus Whitman, Andrew Particka, Christopher Diers +3
In this study, we present and validate the predictive capability of the Physics-Informed Neural Networks (PINNs) methodology for solving a variety of engineering and biological dyn…
stat.ML2025
PBPK-iPINNs: Inverse Physics-Informed Neural Networks for Physiologically Based Pharmacokinetic Brain Models
Charuka D. Wickramasinghe, Krishanthi C. Weerasinghe, Pradeep K. Ranaweera +1
Physics-Informed Neural Networks (PINNs) integrate machine learning with differential equations to solve forward and inverse problems while ensuring that predictions adhere to phys…