4 papers · 1 filter
Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
Xiangming Huang, Guannan Zhang, Lu Lu +2
The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces. Ge…
Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction
Xiaobo Xia, Xiaofeng Liu, Jiale Liu +5
Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning offers transformative potential for large-scale water quality…
Stochastic Operator Network: A Stochastic Maximum Principle Based Approach to Operator Learning
Ryan Bausback, Jingqiao Tang, Lu Lu +2
We develop a novel framework for uncertainty quantification in operator learning, the Stochastic Operator Network (SON). SON combines the stochastic optimal control concepts of the…
Challenges in Training PINNs: A Loss Landscape Perspective
Pratik Rathore, Weimu Lei, Zachary Frangella +2
This paper explores challenges in training Physics-Informed Neural Networks (PINNs), emphasizing the role of the loss landscape in the training process. We examine difficulties in…