133 citations · 542 across the 16 of their papers we have counts for
17 papers
Rapid Uncertainty Quantification on a Latent Field using Fisher Information
Karl Daningburg, A. E. Lovell, Arvind T. Mohan +1
Many inverse problems in physics infer an unobserved field from measurements connected to it through a governing equation. We combine Fisher information with a differentiable solve…
Physics-constrained coupled neural differential equations for one dimensional blood flow modeling
Hunor Csala, Arvind Mohan, Daniel Livescu +1
Computational cardiovascular flow modeling plays a crucial role in understanding blood flow dynamics. While 3D models provide acute details, they are computationally expensive, esp…
Data-Driven Mori-Zwanzig: Reduced Order Modeling of Sparse Sensors Measurements for Boundary Layer Transition
Michael Woodward, Yifeng Tian, Yen Ting Lin +5
Understanding, predicting and controlling laminar-turbulent boundary-layer transition is crucial for the next generation aircraft design. However, in real flight experiments, or wi…
Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach
M. R. Mumpower, M. Li, T. M. Sprouse +3
We present global predictions of the ground state mass of atomic nuclei based on a novel Machine Learning (ML) algorithm. We combine precision nuclear experimental measurements tog…
Full trajectory optimizing operator inference for reduced-order modeling using differentiable programming
Surya Chakrabarti, Arvind T. Mohan, Datta V. Gaitonde +1
Accurate and inexpensive Reduced Order Models (ROMs) for forecasting turbulent flows can facilitate rapid design iterations and thus prove critical for predictive control in engine…
Data-Driven Mori-Zwanzig: Approaching a Reduced Order Model for Hypersonic Boundary Layer Transition
Michael Woodward, Yifeng Tian, Arvind Mohan +5
In this work, we apply, for the first time to spatially inhomogeneous flows, a recently developed data-driven learning algorithm of Mori-Zwanzig (MZ) operators, which is based on a…