4 papers · 1 filter
AutoBalance: An Automatic Balancing Framework for Training Physics-Informed Neural Networks
Kang An, Chenhao Si, Ming Yan +1
Physics-Informed Neural Networks (PINNs) provide a powerful and general framework for solving Partial Differential Equations (PDEs) by embedding physical laws into loss functions.…
Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation
Chen Si, Qianyi Wu, Chaitanya Amballa +1
Realistic sound simulation plays a critical role in many applications. A key element in sound simulation is the room impulse response (RIR), which characterizes how sound propagate…
Convolution-weighting method for the physics-informed neural network: A Primal-Dual Optimization Perspective
Chenhao Si, Ming Yan
Physics-informed neural networks (PINNs) are extensively employed to solve partial differential equations (PDEs) by ensuring that the outputs and gradients of deep learning models…
Complex Physics-Informed Neural Network
Chenhao Si, Ming Yan, Xin Li +1
We propose compleX-PINN, a novel physics-informed neural network (PINN) architecture incorporating a learnable activation function inspired by the Cauchy integral theorem. By optim…