Search Results - "Mohan, Arvind T"

Model reduction and analysis of deep dynamic stall on a plunging airfoil by Mohan, Arvind T., Gaitonde, Datta V., Visbal, Miguel R.

“…•Dynamic stall of MAV wing analyzed using Dynamic Mode Decomposition.•Dominant flow structure oscillating at airfoil frequency with 4 harmonics found.•Dominant…”
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Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach by Mumpower, Matthew, Li, Mengke, Sprouse, Trevor M., Meyer, Bradley S., Lovell, Amy E., Mohan, Arvind T.

“…We present global predictions of the ground state mass of atomic nuclei based on a novel Machine Learning algorithm. We combine precision nuclear experimental…”
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Spatio-temporal deep learning models of 3D turbulence with physics informed diagnostics by Mohan, Arvind T., Tretiak, Dima, Chertkov, Misha, Livescu, Daniel

“…Direct Numerical Simulations (DNSs) of high Reynolds number turbulent flows, encountered in engineering, earth sciences, and astrophysics, are not tractable…”
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Foresight: Analysis That Matters for Data Reduction by Grosset, Pascal, Biwer, Christopher M., Pulido, Jesus, Mohan, Arvind T., Biswas, Ayan, Patchett, John, Turton, Terece L., Rogers, David H., Livescu, Daniel, Ahrens, James

“…As the computation power of supercomputers increases, so does simulation size, which in turn produces orders-of-magnitude more data. Because generated data…”
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Physics-Constrained Generative Adversarial Networks for 3D Turbulence by Tretiak, Dima, Mohan, Arvind T, Livescu, Daniel

“…Generative Adversarial Networks (GANs) have received wide acclaim among the machine learning (ML) community for their ability to generate realistic 2D images…”
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Learning Stable Galerkin Models of Turbulence with Differentiable Programming by Mohan, Arvind T, Nagarajan, Kaushik, Livescu, Daniel

“…Turbulent flow control has numerous applications and building reduced-order models (ROMs) of the flow and the associated feedback control laws is extremely…”
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Full trajectory optimizing operator inference for reduced-order modeling using differentiable programming by Chakrabarti, Surya, Mohan, Arvind T, Gaitonde, Datta V, Livescu, Daniel

“…Accurate and inexpensive Reduced Order Models (ROMs) for forecasting turbulent flows can facilitate rapid design iterations and thus prove critical for…”
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Embedding Hard Physical Constraints in Neural Network Coarse-Graining of 3D Turbulence by Mohan, Arvind T, Lubbers, Nicholas, Livescu, Daniel, Chertkov, Michael

“…In the recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences. A major challenge in deep learning…”
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A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks by Mohan, Arvind T, Gaitonde, Datta V

“…Reduced Order Modeling (ROM) for engineering applications has been a major research focus in the past few decades due to the unprecedented physical insight…”
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Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow by Shankar, Varun, Portwood, Gavin D, Mohan, Arvind T, Mitra, Peetak P, Krishnamurthy, Dilip, Rackauckas, Christopher, Wilson, Lucas A, Schmidt, David P, Viswanathan, Venkatasubramanian

“…In fluid physics, data-driven models to enhance or accelerate solution methods are becoming increasingly popular for many application domains, such as…”
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