40 citations · 61 across the 12 of their papers we have counts for
6 papers · 1 filter
Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
Xiangming Huang, Guannan Zhang, Lu Lu +1
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…
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…
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…
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…
Speeding up and reducing memory usage for scientific machine learning via mixed precision
Joel Hayford, Jacob Goldman-Wetzler, Eric Wang +1
Scientific machine learning (SciML) has emerged as a versatile approach to address complex computational science and engineering problems. Within this field, physics-informed neura…
ROAM: memory-efficient large DNN training via optimized operator ordering and memory layout
Huiyao Shu, Ang Wang, Ziji Shi +3
As deep learning models continue to increase in size, the memory requirements for training have surged. While high-level techniques like offloading, recomputation, and compression…