5 papers
From Non-Convex Self-Concordant Regularization to Scalable Quasi-Newton Training of PINNs
Chenhao Si, Kang An, Shiqian Ma +1
Physics-informed neural networks (PINNs) often require high-accuracy quasi-Newton refinement to obtain reliable partial differential equation solutions, but their residual objectiv…
Demystifying Manifold Constraints in LLM Pre-training
Kang An, Jiaxiang Li, Donald Goldfarb +1
The empirical success of large language model (LLM) pre-training relies heavily on heuristic stabilization techniques, such as explicit normalization layers and weight decay. While…
Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks
Kang An, Chenhao Si, Shiqian Ma +1
Physics-Informed Neural Networks (PINNs) often suffer from slow convergence, training instability, and reduced accuracy on challenging partial differential equations due to the ani…
ASGO: Adaptive Structured Gradient Optimization
Kang An, Yuxing Liu, Rui Pan +4
Training deep neural networks is a structured optimization problem, because the parameters are naturally represented by matrices and tensors rather than by vectors. Under this stru…
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.…