Physics guided machine learning using simplified theories

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

(2020) Elizabeth Qian et al.   PHYSICA D-NONLINEAR PHENOMENA

Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability

(2020) Shaowu Pan et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

Data-driven recovery of hidden physics in reduced order modeling of fluid flows

(2020) Suraj Pawar et al.   PHYSICS OF FLUIDS

A long short-term memory embedding for hybrid uplifted reduced order models

(2020) Shady E. Ahmed et al.   PHYSICA D-NONLINEAR PHENOMENA

Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows

(2020) Suraj Pawar et al.   PHYSICS OF FLUIDS

Interface learning in fluid dynamics: Statistical inference of closures within micro–macro-coupling models

(2020) Suraj Pawar et al.   PHYSICS OF FLUIDS

Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems

(2020) Nikhil Muralidhar et al.   Big Data

Probabilistic neural networks for fluid flow surrogate modeling and data recovery

(2020) Romit Maulik et al.   Physical Review Fluids

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

(2019) Yinhao Zhu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Sub-grid scale model classification and blending through deep learning

(2019) Romit Maulik et al.   JOURNAL OF FLUID MECHANICS

Fast flow field prediction over airfoils using deep learning approach

(2019) Vinothkumar Sekar et al.   PHYSICS OF FLUIDS

Prediction of aerodynamic flow fields using convolutional neural networks

(2019) Saakaar Bhatnagar et al.   COMPUTATIONAL MECHANICS

Modal Analysis of Fluid Flows: Applications and Outlook

(2019) Kunihiko Taira et al.   AIAA JOURNAL

Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

(2019) Nicholas Geneva et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Deep neural networks for data-driven LES closure models

(2019) Andrea Beck et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

(2018) Jaideep Pathak et al.   PHYSICAL REVIEW LETTERS

Projection-based model reduction: Formulations for physics-based machine learning

(2018) Renee Swischuk et al.   COMPUTERS & FLUIDS

Subgrid modelling for two-dimensional turbulence using neural networks

(2018) R. Maulik et al.   JOURNAL OF FLUID MECHANICS

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Fast divergence-conforming reduced basis methods for steady Navier–Stokes flow

(2018) Eivind Fonn et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Comparative Study of Three-Dimensional Wing Drag Minimization by Different Optimization Techniques

(2009) Boris Epstein et al.   JOURNAL OF AIRCRAFT