SEA-CROGS Grant: Scalable, Efficient and Accelerated Causal Reasoning Operators, Graphs and Spikes for Earth and Embedded Systems
MURI AFOSR Grant: Learning and Meta-Learning of Partial Differential Equations via Physics-Informed Neural Networks: Theory, Algorithms, and Applications
DARPA Grant: NeuroNSE: A Data-Informed Hybrid Framework for Simulating Bluff Body Turbulence via PINNs and Spectral Elements
ONR Vannevar Bush Grant: The Next Generation of Operator Regression Networks: Theory, Algorithms, Applications
NIH/National Heart, Lung, and Blood Institute Grant: Multifidelity and multiscale modeling of the spleen function in hereditary spherocytosis and sickle cell disease with in vitro and ex vivo validations
University of Rochester/NIH Grant: CRCNS: Waste-clearance flows in the brain inferred using physics-informed neural networks – Budget Revision
Clemson University/DOE Grant: Artificially Intelligent Manufacturing Paradigm for Composites (AIM for Composites)
Yale/NIH Grant: Neural Operator Learning to Predict Aneurysmal Growth and Outcomes
Sandia National Laboratories Grant: TimeGEMM: Time Acceleration via Generalizable Machine-Learned Models Enabling tractable long timescales in materials prediction
Pacific Northwest National Laboratories Grant: PNNL Joint Appointment Agreement
Karagozian & Case/DTRA Grant: NINNs: Numerics-Informed Neural Networks, Phase II year 2
Army Grants: Foundational aerothermoelastic models for intelligent design of in-flight adaptive aerodynamics in extreme hypersonic environments & Scalable DeepONets for Fundamental Studies of Hypersonic Flows
