Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows
This work addresses computational efficiency for simulating complex physical phenomena, but it is incremental as it builds on existing PINN and transfer learning methods.
The paper tackled the problem of simulating stochastic branched flows in random wave dynamics using Physics-Informed Neural Networks (PINNs) with transfer learning, achieving significant computational speedups compared to standard PINNs trained from scratch.
Physics-Informed Neural Networks (PINNs) offer a promising approach to solving differential equations and, more generally, to applying deep learning to problems in the physical sciences. We adopt a recently developed transfer learning approach for PINNs and introduce a multi-head model to efficiently obtain accurate solutions to nonlinear systems of ordinary differential equations with random potentials. In particular, we apply the method to simulate stochastic branched flows, a universal phenomenon in random wave dynamics. Finally, we compare the results achieved by feed forward and GAN-based PINNs on two physically relevant transfer learning tasks and show that our methods provide significant computational speedups in comparison to standard PINNs trained from scratch.