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Physics-Informed Machine Learning in Biomedical Science and Engineering
Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey +1
Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws wit…
AMORE: Adaptive Multi-Output Operator Network for Stiff Chemical Kinetics
Kamaljyoti Nath, Additi Pandey, Bryan T. Susi +2
Time integration of stiff systems is a primary source of computational cost in combustion, hypersonics, and other reactive transport systems. This stiffness can introduce time scal…
DeepSeek vs. ChatGPT vs. Claude: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks
Qile Jiang, Zhiwei Gao, George Em Karniadakis
Large Language Models (LLMs) have emerged as powerful tools for tackling a wide range of problems, including those in scientific computing, particularly in solving partial differen…
Connecting the geometry and dynamics of many-body complex systems with message passing neural operators
Nicholas A. Gabriel, Neil F. Johnson, George Em Karniadakis
The relationship between scale transformations and dynamics established by renormalization group techniques is a cornerstone of modern physical theories, from fluid mechanics to el…
DeepVIVONet: Using deep neural operators to optimize sensor locations with application to vortex-induced vibrations
Ruyin Wan, Ehsan Kharazmi, Michael S Triantafyllou +1
We introduce DeepVIVONet, a new framework for optimal dynamic reconstruction and forecasting of the vortex-induced vibrations (VIV) of a marine riser, using field data. We demonstr…
Fusion-DeepONet: A Data-Efficient Neural Operator for Geometry-Dependent Hypersonic and Supersonic Flows
Ahmad Peyvan, Varun Kumar, George Em Karniadakis
Shape optimization is essential in aerospace vehicle design, including reentry systems, and propulsion system components, as it directly influences aerodynamic efficiency, structur…