Shaowu Pan
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View article: CFDLLMBench: A Benchmark Suite for Evaluating Large Language Models in Computational Fluid Dynamics
CFDLLMBench: A Benchmark Suite for Evaluating Large Language Models in Computational Fluid Dynamics Open
Large Language Models (LLMs) have demonstrated strong performance across general NLP tasks, but their utility in automating numerical experiments of complex physical system -- a critical and labor-intensive component -- remains underexplor…
View article: Foam-Agent 2.0: An End-to-End Composable Multi-Agent Framework for Automating CFD Simulation in OpenFOAM
Foam-Agent 2.0: An End-to-End Composable Multi-Agent Framework for Automating CFD Simulation in OpenFOAM Open
Computational Fluid Dynamics (CFD) is an essential simulation tool in engineering, yet its steep learning curve and complex manual setup create significant barriers. To address these challenges, we introduce Foam-Agent, a multi-agent frame…
View article: Code2MCP: Transforming Code Repositories into MCP Services
Code2MCP: Transforming Code Repositories into MCP Services Open
The Model Context Protocol (MCP) aims to create a standard for how Large Language Models use tools. However, most current research focuses on selecting tools from an existing pool. A more fundamental, yet largely overlooked, problem is how…
View article: UniFoil: A Universal Dataset of Airfoils in Transitional and Turbulent Regimes for Subsonic and Transonic Flows
UniFoil: A Universal Dataset of Airfoils in Transitional and Turbulent Regimes for Subsonic and Transonic Flows Open
We present UniFoil, a large publicly available universal airfoil dataset based on Reynolds-averaged Navier-Stokes (RANS) simulations. It contains over 500,000 samples spanning a wide range of Reynolds and Mach numbers, capturing both trans…
View article: Foam-Agent: Towards Automated Intelligent CFD Workflows
Foam-Agent: Towards Automated Intelligent CFD Workflows Open
Computational Fluid Dynamics (CFD) is an essential simulation tool in various engineering disciplines, but it often requires substantial domain expertise and manual configuration, creating barriers to entry. We present Foam-Agent, a multi-…
View article: Beyond the Kolmogorov Barrier: A Learnable Weighted Hybrid Autoencoder for Model Order Reduction
Beyond the Kolmogorov Barrier: A Learnable Weighted Hybrid Autoencoder for Model Order Reduction Open
Representation learning for high-dimensional, complex physical systems aims to identify a low-dimensional intrinsic latent space, which is crucial for reduced-order modeling and modal analysis. To overcome the well-known Kolmogorov barrier…
View article: Wavefield Reconstruction of Distributed Acoustic Sensing: Lossy Compression, Wavefield Separation, and Edge Computing
Wavefield Reconstruction of Distributed Acoustic Sensing: Lossy Compression, Wavefield Separation, and Edge Computing Open
Distributed acoustic sensing (DAS) presents challenges and opportunities for seismological research and data management. This study explores wavefield reconstruction using deep learning methods for data compression and wavefield separation…
View article: Learning Noise-Robust Stable Koopman Operator for Control with Hankel DMD
Learning Noise-Robust Stable Koopman Operator for Control with Hankel DMD Open
We propose a noise-robust learning framework for the Koopman operator of nonlinear dynamical systems, with guaranteed long-term stability and improved model performance for better model-based predictive control tasks. Unlike some existing …
View article: <i>In-situ</i> forming ultra-mechanically sensitive materials for high-sensitivity stretchable fiber strain sensors
<i>In-situ</i> forming ultra-mechanically sensitive materials for high-sensitivity stretchable fiber strain sensors Open
Fiber electronics with flexible and weavable features can be easily integrated into textiles for wearable applications. However, due to small sizes and curved surfaces of fiber materials, it remains challenging to load robust active layers…
View article: Grad–Shafranov equilibria via data-free physics informed neural networks
Grad–Shafranov equilibria via data-free physics informed neural networks Open
A large number of magnetohydrodynamic (MHD) equilibrium calculations are often required for uncertainty quantification, optimization, and real-time diagnostic information, making MHD equilibrium codes vital to the field of plasma physics. …
View article: PyKoopman: A Python Package for Data-DrivenApproximation of the Koopman Operator
PyKoopman: A Python Package for Data-DrivenApproximation of the Koopman Operator Open
PyKoopman is a Python package for the data-driven approximation of the Koopman operator associated with a dynamical systems.The Koopman operator is a principled linear embedding of nonlinear dynamics and facilitates the prediction, estimat…
View article: Grad-Shafranov equilibria via data-free physics informed neural networks
Grad-Shafranov equilibria via data-free physics informed neural networks Open
A large number of magnetohydrodynamic (MHD) equilibrium calculations are often required for uncertainty quantification, optimization, and real-time diagnostic information, making MHD equilibrium codes vital to the field of plasma physics. …
View article: PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator Open
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 nonlinear dynamics and facilitates the prediction, estimat…
View article: On the lifting and reconstruction of nonlinear systems with multiple invariant sets
On the lifting and reconstruction of nonlinear systems with multiple invariant sets Open
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 typically linearly reconstructed from the Koopman eigenfunctions. D…
View article: Correction: Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning
Correction: Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning Open
http://deepblue.lib.umich.edu/bitstream/2027.42/174074/1/42774_2022_Article_118.pdf
View article: Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data Open
High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional subspace. Engineering applications for modeling, characterization, design, and control of such large-scale systems often rely on dimensionality reduction t…
View article: Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning
Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning Open
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. In general, approximate solutions can be obtained by itera…
View article: Discretization-independent surrogate modeling over complex geometries using hypernetworks and implicit representations
Discretization-independent surrogate modeling over complex geometries using hypernetworks and implicit representations Open
Numerical solutions of partial differential equations (PDEs) require expensive simulations, limiting their application in design optimization, model-based control, and large-scale inverse problems. Surrogate modeling techniques seek to dec…
View article: Non-linear Independent Dual System (NIDS) for Discretization-independent Surrogate Modeling over Complex Geometries.
Non-linear Independent Dual System (NIDS) for Discretization-independent Surrogate Modeling over Complex Geometries. Open
Numerical solutions of partial differential equations (PDEs) require expensive simulations, limiting their application in design optimization routines, model-based control, or solution of large-scale inverse problems. Existing Convolutiona…
View article: Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics Open
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 neural network, such that the network not only conforms to t…
View article: Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces
Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces Open
View article: Robust and Interpretable Learning for Operator-Theoretic Modeling of Non-linear Dynamics
Robust and Interpretable Learning for Operator-Theoretic Modeling of Non-linear Dynamics Open
Non-linear dynamical systems are of significant interest to a wide range of science and engineering communities. This dissertation is focused on the advancement of theory and algorithms for operator-theoretic modeling and decomposition of …
View article: Sparsity-promoting algorithms for the discovery of informative Koopman invariant subspaces
Sparsity-promoting algorithms for the discovery of informative Koopman invariant subspaces Open
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 such as the dynamic mode decomposition (DMD) and its linear …
View article: Thermal‐Disrupting Interface Mitigates Intercellular Cohesion Loss for Accurate Topical Antibacterial Therapy
Thermal‐Disrupting Interface Mitigates Intercellular Cohesion Loss for Accurate Topical Antibacterial Therapy Open
Bacterial infections remain a leading threat to global health because of the misuse of antibiotics and the rise in drug‐resistant pathogens. Although several strategies such as photothermal therapy and magneto‐thermal therapy can suppress …
View article: Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability
Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability Open
The Koopman operator has emerged as a powerful tool for the analysis of\nnonlinear dynamical systems as it provides coordinate transformations to\nglobally linearize the dynamics. While recent deep learning approaches have\nbeen useful in …
View article: Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning
Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning Open
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. In general, approximate solutions can be obtained by itera…