Nature

Physics-Informed Neural Networks and Differential Equations

Physics-informed neural networks (PINNs) represent a burgeoning paradigm in computational science, whereby deep learning frameworks are augmented with explicit physical laws to solve both forward and ...
EurekAlert!

Physics-informed neural networks building blocks for Heston model (IMAGE)

It shows the schematic of the physics-informed neural network algorithm for pricing European options under the Heston model. The market price of risk is taken to be λ=0. Automatic differentiation is ...
Frontiers

Hybrid Mechanistic–AI Modeling with Physics-Trained Neural Networks for Patient Digital Twins

Patient digital twins aim to create computational replicas of an individual’s physiology that can predict disease trajectories and treatment response.
EurekAlert!

Transfer learning-enhanced physics-informed neural network (TLE-PINN): A breakthrough in melt pool prediction for laser melting

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...