Search Results for author:
Found 16 papers, 7 papers with code
Variational Bayesian Optimal Experimental Design with Normalizing Flows
Variational OED (vOED), in contrast, estimates a lower bound of the EIG without likelihood evaluations by approximating the posterior distributions with variational forms, and then tightens the bound by optimizing its variational parameters.
Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition
We introduce a comprehensive data-driven framework aimed at enhancing the modeling of physical systems, employing inference techniques and machine learning enhancements.
CoCoGen: Physically-Consistent and Conditioned Score-based Generative Models for Forward and Inverse Problems
Secondly, we showcase the potential and versatility of score-based generative models in various physics tasks, specifically highlighting surrogate modeling as well as probabilistic field reconstruction and inversion from sparse measurements.
Easy attention: A simple self-attention mechanism for transformer-based time-series reconstruction and prediction
To improve the robustness of transformer neural networks used for temporal-dynamics prediction of chaotic systems, we propose a novel attention mechanism called easy attention which we demonstrate in time-series reconstruction and prediction.
On the lifting and reconstruction of nonlinear systems with multiple invariant sets
The Koopman operator provides a linear perspective on non-linear dynamics by focusing on the evolution of observables in an invariant subspace.
Non-intrusive Balancing Transformation of Highly Stiff Systems with Lightly-damped Impulse Response
Balanced truncation (BT) is a model reduction method that utilizes a coordinate transformation to retain eigen-directions that are highly observable and reachable.
Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical Systems
Simulations of complex physical systems are typically realized by discretizing partial differential equations (PDEs) on unstructured meshes.
Disentangling Generative Factors of Physical Fields Using Variational Autoencoders
We illustrate comparisons between disentangled and entangled representations by juxtaposing learned latent distributions and the true generative factors in a model porous flow problem.
Discretization-independent surrogate modeling over complex geometries using hypernetworks and implicit representations
Test cases include a vehicle-aerodynamics problem with complex geometry and limited training data, with a design-variable hypernetwork performing best, with a competitive time-to-best-model despite a much greater parameter count.
Variational Encoders and Autoencoders : Information-theoretic Inference and Closed-form Solutions
This work develops problem statements related to encoders and autoencoders with the goal of elucidating variational formulations and establishing clear connections to information-theoretic concepts.
Variational Inference
Information Theory
Information Theory
Probability
62B10
G.3; H.1.1
Sparsity-promoting algorithms for the discovery of informative Koopman invariant subspaces
Despite being endowed with a richer dictionary of nonlinear observables, nonlinear variants of the DMD, such as extended/kernel dynamic mode decomposition (EDMD/KDMD) are seldom applied to large-scale problems primarily due to the difficulty of discerning the Koopman invariant subspace from thousands of resulting Koopman eigenmodes.
Multi-level Convolutional Autoencoder Networks for Parametric Prediction of Spatio-temporal Dynamics
A fully-connected network is used as the third level to learn the mapping between these latent variables and the global parameters from training data, and predict them for new parameters.
Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability
In this work, we formalize the problem of learning the continuous-time Koopman operator with deep neural networks in a measure-theoretic framework.
Quad-rotor Flight Simulation in Realistic Atmospheric Conditions
A reduced-order version of the atmospheric boundary layer data as well as the popular Dryden model are used to assess the impact of accuracy of the wind field model on the predicted vehicle performance and trajectory.
Fluid Dynamics Atmospheric and Oceanic Physics Applied Physics
Long-time predictive modeling of nonlinear dynamical systems using neural networks
We study the use of feedforward neural networks (FNN) to develop models of nonlinear dynamical systems from data.
Data-driven Discovery of Closure Models
In this work, we present a framework of operator inference to extract the governing dynamics of closure from data in a compact, non-Markovian form.
