Search Results for author:
Found 29 papers, 14 papers with code
Physically Interpretable Representation and Controlled Generation for Turbulence Data
Computational Fluid Dynamics (CFD) plays a pivotal role in fluid mechanics, enabling precise simulations of fluid behavior through partial differential equations (PDEs).
Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices
The second is rank allocation, where we choose the ranks of the blocks in each level, subject to the total rank having a given value, which preserves the total storage needed for the MLR matrix.
Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source
Significant advances in utilizing deep learning for anomaly detection have been made in recent years.
Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks
Our numerical results demonstrate the accuracy and generalizability of our method in physiological geometries comprising a variety of anatomies and boundary conditions.
Physics-based parameterized neural ordinary differential equations: prediction of laser ignition in a rocket combustor
In this work, we present a novel physics-based data-driven framework for reduced-order modeling of laser ignition in a model rocket combustor based on parameterized neural ordinary differential equations (PNODE).
Coincident Learning for Unsupervised Anomaly Detection
While complex systems often have a wealth of data, labeled anomalies are typically rare (or even nonexistent) and expensive to acquire.
Probabilistic partition of unity networks for high-dimensional regression problems
We explore the probabilistic partition of unity network (PPOU-Net) model in the context of high-dimensional regression problems and propose a general framework focusing on adaptive dimensionality reduction.
A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations
A simple but highly effective method "Language Information Removal (LIR)" factors out language identity information from semantic related components in multilingual representations pre-trained on multi-monolingual data.
Hierarchical Orthogonal Factorization: Sparse Least Squares Problems
In this work, we develop a fast hierarchical solver for solving large, sparse least squares problems.
Numerical Analysis Numerical Analysis 65F08 (Primary), 65F25, 65F50, 65Y20, 65F05 (Secondary) G.1.3
Universal Sentence Representations Learning with Conditional Masked Language Model
This paper presents a novel training method, Conditional Masked Language Modeling (CMLM), to effectively learn sentence representations on large scale unlabeled corpora.
Universal Sentence Representation Learning with Conditional Masked Language Model
This paper presents a novel training method, Conditional Masked Language Modeling (CMLM), to effectively learn sentence representations on large scale unlabeled corpora.
Application of Deep Learning-based Interpolation Methods to Nearshore Bathymetry
We propose a Deep Neural Network (DNN) to compute posterior estimates of the nearshore bathymetry, as well as a conditional Generative Adversarial Network (cGAN) that samples from the posterior distribution.
Multi-Constitutive Neural Network for Large Deformation Poromechanics Problem
We propose a novel method "multi-constitutive neural network" (MCNN) such that one model can solve several different constitutive laws.
Anomaly Detection with Domain Adaptation
We study the problem of semi-supervised anomaly detection with domain adaptation.
Learning Constitutive Relations using Symmetric Positive Definite Neural Networks
We present the Cholesky-factored symmetric positive definite neural network (SPD-NN) for modeling constitutive relations in dynamical equations.
Numerical Analysis Numerical Analysis
Physics Constrained Learning for Data-driven Inverse Modeling from Sparse Observations
Our approach allows for the potential to solve and accelerate a wide range of data-driven inverse modeling, where the physical constraints are described by PDEs and need to be satisfied accurately.
Numerical Analysis Numerical Analysis
Memory Augmented Generative Adversarial Networks for Anomaly Detection
Decoded memory units in MEMGAN are more interpretable and disentangled than previous methods, which further demonstrates the effectiveness of the memory mechanism.
Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection
In this paper, we investigate algorithms for anomaly detection.
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising
Text summarization aims to extract essential information from a piece of text and transform the text into a concise version.
Time-lapse Full Waveform Inversion for Subsurface Flow Problems with Intelligent Automatic Differentiation
We describe a novel framework for PDE (partial-differential-equation)-constrained full-waveform inversion (FWI) that estimates parameters of subsurface flow processes, such as rock permeability and porosity, using time-lapse observed data.
Geophysics
Learning Hidden Dynamics using Intelligent Automatic Differentiation
Numerical tests demonstrate the feasibility of IAD for learning hidden dynamics in complicated systems of PDEs; additionally, by incorporating custom built state adjoint method codes in IAD, we significantly accelerate the forward and inverse simulation.
Numerical Analysis Numerical Analysis
Adversarial Numerical Analysis for Inverse Problems
Many scientific and engineering applications are formulated as inverse problems associated with stochastic models.
Numerical Analysis Numerical Analysis
Embedding Imputation with Grounded Language Information
In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph.
Out-of-Vocabulary Embedding Imputation with Grounded Language Information by Graph Convolutional Networks
In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph.
Predictive Modeling with Learned Constitutive Relations from Indirect Observations
Its counterparts, like piecewise linear functions and radial basis functions, are compared, and the strength of neural networks is explored.
Numerical Analysis Numerical Analysis Computational Physics
PBBFMM3D: a parallel black-box algorithm for kernel matrix-vector multiplication
We introduce a parallel method that provably requires $O(N)$ operations to reduce the computation cost.
Mathematical Software
An Algebraic Sparsified Nested Dissection Algorithm Using Low-Rank Approximations
We evaluate the algorithm on some large problems show it exhibits near-linear scaling.
Numerical Analysis
Calibrating Multivariate Lévy Processes with Neural Networks
Traditionally this problem can be solved with nonparametric estimation using the empirical characteristic functions (ECF), assuming certain regularity, and results to date are mostly in 1D.
Block Basis Factorization for Scalable Kernel Matrix Evaluation
Kernel methods are widespread in machine learning; however, they are limited by the quadratic complexity of the construction, application, and storage of kernel matrices.
