1.1k citations · 2.5k across the 14 of their papers we have counts for
14 papers
Foam-Agent: A Large Language Model-Based Multi-Agent Framework for Automating Computational Fluid Dynamics Workflows
Ling Yue, Nithin Somasekharan, Tingwen Zhang +4
Computational fluid dynamics (CFD) has been the main workhorse of computational physics, yet its steep learning curve and fragmented, multi-stage workflow create significant barrie…
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator
Shaowu Pan, Eurika Kaiser, Brian M. de Silva +2
PyKoopman is a Python package for the data-driven approximation of the Koopman operator associated with a dynamical system. The Koopman operator is a principled linear embedding of…
On the lifting and reconstruction of nonlinear systems with multiple invariant sets
Shaowu Pan, Karthik Duraisamy
The Koopman operator provides a linear perspective on non-linear dynamics by focusing on the evolution of observables in an invariant subspace. Observables of interest are typicall…
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data
Shaowu Pan, Steven L. Brunton, J. Nathan Kutz
High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional subspace. Engineering applications for modeling, characterization, design, and control of such l…
Discretization-independent surrogate modeling over complex geometries using hypernetworks and implicit representations
James Duvall, Karthik Duraisamy, Shaowu Pan
Numerical solutions of partial differential equations (PDEs) require expensive simulations, limiting their application in design optimization, model-based control, and large-scale…
Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
Weiqi Ji, Weilun Qiu, Zhiyu Shi +2
Recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of the n…
Sparsity-promoting algorithms for the discovery of informative Koopman invariant subspaces
Shaowu Pan, Nicholas Arnold-Medabalimi, Karthik Duraisamy
Koopman decomposition is a non-linear generalization of eigen-decomposition, and is being increasingly utilized in the analysis of spatio-temporal dynamics. Well-known techniques s…
Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning
Qi Gao, Shaowu Pan, Hongping Wang +2
Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained.…
Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data
Luning Sun, Han Gao, Shaowu Pan +1
Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation into the finite-dimensional algebraic system…
Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability
Shaowu Pan, Karthik Duraisamy
The Koopman operator has emerged as a powerful tool for the analysis of nonlinear dynamical systems as it provides coordinate transformations to globally linearize the dynamics. Wh…
Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks
Yaser Afshar, Saakaar Bhatnagar, Shaowu Pan +2
An approximation model based on convolutional neural networks (CNNs) is proposed for flow field predictions. The CNN is used to predict the velocity and pressure field in unseen fl…
On the Structure of Time-delay Embedding in Linear Models of Non-linear Dynamical Systems
Shaowu Pan, Karthik Duraisamy
This work addresses fundamental issues related to the structure and conditioning of linear time-delayed models of non-linear dynamics on an attractor. While this approach has been…
Long-time predictive modeling of nonlinear dynamical systems using neural networks
Shaowu Pan, Karthik Duraisamy
We study the use of feedforward neural networks (FNN) to develop models of nonlinear dynamical systems from data. Emphasis is placed on predictions at long times, with limited data…
Data-driven Discovery of Closure Models
Shaowu Pan, Karthik Duraisamy
Derivation of reduced order representations of dynamical systems requires the modeling of the truncated dynamics on the retained dynamics. In its most general form, this so-called…