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 ...

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

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 ...

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 ...

One of the key steps in developing new materials is property identification, which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A ...