PINNACLE: PINN Adaptive ColLocation and Experimental points selection

arXiv:2404.07662v1 Announce Type: new
Abstract: Physics-Informed Neural Networks (PINNs), which incorporate PDEs as soft constraints, train with a composite loss function that contains multiple training point types: different types of collocation points chosen during training to enforce each PDE and initial/boundary conditions, and experimental points which are usually costly to obtain via experiments or simulations. Training PINNs using this loss function is challenging as it typically requires selecting large numbers of points of different types, each with different training dynamics. …

abstract arxiv constraints cs.ai cs.lg experimental function loss multiple networks neural networks physics physics.comp-ph physics-informed pinn train training type types via