- (active) PI: Inertial neural surrogates for stable dynamical prediction, Data intensive scientific machine learning, DOE-ASCR FOA2493, Amount: $3,457,000.
- (active) Co-PI: AI emulator assisted data assimilation, Future computing, LDRD-Prime, Argonne National Laboratory, U.S. Department of Energy, Amount: $280,000. (PI - Rao Kotamarthi)
- (active) Senior personnel: RAPIDS2:A SciDAC Institute for Computer Science, Data, and Artificial Intelligence, U.S. Department of Energy, Amount: $305,000. (PI - Rob Ross)
- (Finished) Co-PI: A Scalable, Energy Efficient HPC Environment for AI-Enabled Science, Collaborative PPoSS funding, National Science Foundation. (PI - Zhiling Lan)
- (Finished) PI: Margaret-Butler Fellowship project: Scalable machine learning for turbulence closure and reduced-order modeling (2 yr postdoctoral fellowship), Amount: $300,000.
- (Finished) PI: SambaWF: Highly resolved surrogate models for weather forecasting, LDRD-Expedition, Argonne National Laboratory, U.S. Department of Energy, Amount: $50,000.
- PythonFOAM, In-situ data analyses with OpenFOAM and Python.
- TensorFlowFOAM, A framework that enables the deployment of TensorFlow deep learning models and partial differential equation solutions concurrently in OpenFOAM.
- PAR-RL, A framework that leverages the Ray library to deploy scalable deep reinforcement learning for arbitrary scientific environments on leadership class machines.
- PyParSVD, A Parallelized, streaming, and randomized implementation of the SVD for Python using mpi4py.
- PySPOD, A package to compute the spectral proper orthogonal decomposition in Python.