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09/07/2023
An Element-wise RSAV Algorithm for Unconstrained Optimization Problems
We present a novel optimization algorithm, element-wise relaxed scalar a...
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08/27/2023
Multi-Subdomain Adversarial Network for Cross-Subject EEG-based Emotion Recognition
The individual difference between subjects is significant in EEG-based e...
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06/09/2023
Energy-Dissipative Evolutionary Deep Operator Neural Networks
Energy-Dissipative Evolutionary Deep Operator Neural Network is an opera...
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06/01/2023
Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles
This paper designs surrogate models with uncertainty quantification capa...
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05/11/2023
Numerical Stability for Differential Equations with Memory
In this work, we systematically investigate linear multi-step methods fo...
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04/06/2023
HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
Due to the complex behavior arising from non-uniqueness, symmetry, and b...
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03/03/2023
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
This paper presents NSGA-PINN, a multi-objective optimization framework ...
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01/29/2023
On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators
This paper designs an Operator Learning framework to approximate the dyn...
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11/20/2022
Non-reversible Parallel Tempering for Deep Posterior Approximation
Parallel tempering (PT), also known as replica exchange, is the go-to wo...
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09/21/2022
DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems
This paper develops a Deep Graph Operator Network (DeepGraphONet) framew...
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09/01/2022
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecu
We introduce an explorative active learning (AL) algorithm based on Gaus...
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07/24/2022
AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems
In this work, we propose an adaptive sparse learning algorithm that can ...
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07/21/2022
Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems
The flow-driven spectral chaos (FSC) is a recently developed method for ...
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05/30/2022
Federated X-Armed Bandit
This work establishes the first framework of federated 𝒳-armed bandit, w...
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04/11/2022
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification
Multi-fidelity modelling arises in many situations in computational scie...
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04/07/2022
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochasti
A new data-driven method for operator learning of stochastic differentia...
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04/06/2022
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations
In this work, a Gaussian process regression(GPR) model incorporated with...
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02/27/2022
Federated Online Sparse Decision Making
This paper presents a novel federated linear contextual bandits model, w...
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02/20/2022
Interacting Contour Stochastic Gradient Langevin Dynamics
We propose an interacting contour stochastic gradient Langevin dynamics ...
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02/15/2022
DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories
This paper proposes a new data-driven method for the reliable prediction...
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01/22/2022
glassoformer: a query-sparse transformer for post-fault power grid voltage prediction
We propose GLassoformer, a novel and efficient transformer architecture ...
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12/09/2021
On Convergence of Federated Averaging Langevin Dynamics
We propose a federated averaging Langevin algorithm (FA-LD) for uncertai...
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11/22/2021
Deformation Robust Roto-Scale-Translation Equivariant CNNs
Incorporating group symmetry directly into the learning process has prov...
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09/18/2021
PCNN: A physics-constrained neural network for multiphase flows
The present study develops a physics-constrained neural network (PCNN) t...
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09/09/2021
DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks
Deep learning-based surrogate modeling is becoming a promising approach ...
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05/21/2021
Flow-driven spectral chaos (FSC) method for long-time integration of second-order stochastic dynamical systems
For decades, uncertainty quantification techniques based on the spectral...
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03/14/2021
Implementing contact angle boundary conditions for second-order Phase-Field models of wall-bounded multiphase flows
In the present work, a general formulation is proposed to implement the ...
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02/13/2021
A consistent and conservative Phase-Field model for thermo-gas-liquid-solid flows including liquid-solid phase change
In the present study, a consistent and conservative Phase-Field model is...
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02/02/2021
Robust data-driven discovery of partial differential equations with time-dependent coefficients
In this work, we propose a robust Bayesian sparse learning algorithm bas...
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01/12/2021
A consistent and conservative model and its scheme for N-phase-M-component incompressible flows
In the present work, we propose a consistent and conservative model for ...
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12/02/2020
Flow-driven spectral chaos (FSC) method for simulating long-time dynamics of arbitrary-order non-linear stochastic dynamical systems
Uncertainty quantification techniques such as the time-dependent general...
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11/03/2020
Single Shot Reversible GAN for BCG artifact removal in simultaneous EEG-fMRI
Simultaneous EEG-fMRI acquisition and analysis technology has been widel...
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10/19/2020
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions
We propose an adaptively weighted stochastic gradient Langevin dynamics ...
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10/07/2020
Predicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks
Evaluating the mechanical response of fiber-reinforced composites can be...
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10/04/2020
Spatial Damage Characterization in Self-Sensing Materials via Neural Network-Aided Electrical Impedance Tomography: A Computational Study
Continuous structural health monitoring (SHM) and integrated nondestruct...
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10/03/2020
An adaptive Hessian approximated stochastic gradient MCMC method
Bayesian approaches have been successfully integrated into training deep...
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10/03/2020
MFPC-Net: Multi-fidelity Physics-Constrained Neural Process
In this work, we propose a network which can utilize computational cheap...
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10/02/2020
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
Replica exchange stochastic gradient Langevin dynamics (reSGLD) has show...
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09/03/2020
Augmented Gaussian Random Field: Theory and Computation
We provide a rigorous theoretical foundation for incorporating data of o...
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08/12/2020
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Replica exchange Monte Carlo (reMC), also known as parallel tempering, i...
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08/03/2020
Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression
We propose a data fusion method based on multi-fidelity Gaussian process...
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05/23/2020
Peri-Net-Pro: The neural processes with quantified uncertainty for crack patterns
This paper uses the peridynamic theory, which is well-suited to crack st...
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05/11/2020
Multi-Fidelity Gaussian Process based Empirical Potential Development for Si:H Nanowires
In material modeling, the calculation speed using the empirical potentia...
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04/28/2020
RotEqNet: Rotation-Equivariant Network for Fluid Systems with Symmetric High-Order Tensors
In the recent application of scientific modeling, machine learning model...
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02/17/2020
A Sparse Deep Factorization Machine for Efficient CTR prediction
Click-through rate (CTR) prediction is a crucial task in online display ...
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10/23/2019
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
We propose a novel adaptive empirical Bayesian (AEB) method for sparse d...
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10/18/2019
Inverse modeling of hydrologic parameters in CLM4 via generalized polynomial chaos in the Bayesian framework
In this study, the applicability of generalized polynomial chaos (gPC) e...
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10/07/2019
Gaussian Process Assisted Active Learning of Physical Laws
In many areas of science and engineering, discovering the governing diff...
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07/22/2019
Efficient Deep Learning Techniques for Multiphase Flow Simulation in Heterogeneous Porous Media
We present efficient deep learning techniques for approximating flow and...
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07/17/2019
