We present a novel optimization algorithm, element-wise relaxed scalar
a...
The individual difference between subjects is significant in EEG-based
e...
Energy-Dissipative Evolutionary Deep Operator Neural Network is an opera...
This paper designs surrogate models with uncertainty quantification
capa...
In this work, we systematically investigate linear multi-step methods fo...
Due to the complex behavior arising from non-uniqueness, symmetry, and
b...
This paper presents NSGA-PINN, a multi-objective optimization framework ...
This paper designs an Operator Learning framework to approximate the dyn...
Parallel tempering (PT), also known as replica exchange, is the go-to
wo...
This paper develops a Deep Graph Operator Network (DeepGraphONet) framew...
We introduce an explorative active learning (AL) algorithm based on Gaus...
In this work, we propose an adaptive sparse learning algorithm that can ...
The flow-driven spectral chaos (FSC) is a recently developed method for
...
This work establishes the first framework of federated 𝒳-armed
bandit, w...
Multi-fidelity modelling arises in many situations in computational scie...
A new data-driven method for operator learning of stochastic differentia...
In this work, a Gaussian process regression(GPR) model incorporated with...
This paper presents a novel federated linear contextual bandits model, w...
We propose an interacting contour stochastic gradient Langevin dynamics
...
This paper proposes a new data-driven method for the reliable prediction...
We propose GLassoformer, a novel and efficient transformer architecture
...
We propose a federated averaging Langevin algorithm (FA-LD) for uncertai...
Incorporating group symmetry directly into the learning process has prov...
The present study develops a physics-constrained neural network (PCNN) t...
Deep learning-based surrogate modeling is becoming a promising approach ...
For decades, uncertainty quantification techniques based on the spectral...
In the present work, a general formulation is proposed to implement the
...
In the present study, a consistent and conservative Phase-Field model is...
In this work, we propose a robust Bayesian sparse learning algorithm bas...
In the present work, we propose a consistent and conservative model for
...
Uncertainty quantification techniques such as the time-dependent general...
Simultaneous EEG-fMRI acquisition and analysis technology has been widel...
We propose an adaptively weighted stochastic gradient Langevin dynamics
...
Evaluating the mechanical response of fiber-reinforced composites can be...
Continuous structural health monitoring (SHM) and integrated nondestruct...
Bayesian approaches have been successfully integrated into training deep...
In this work, we propose a network which can utilize computational cheap...
Replica exchange stochastic gradient Langevin dynamics (reSGLD) has show...
We provide a rigorous theoretical foundation for incorporating data of
o...
Replica exchange Monte Carlo (reMC), also known as parallel tempering, i...
We propose a data fusion method based on multi-fidelity Gaussian process...
This paper uses the peridynamic theory, which is well-suited to crack
st...
In material modeling, the calculation speed using the empirical potentia...
In the recent application of scientific modeling, machine learning model...
Click-through rate (CTR) prediction is a crucial task in online display
...
We propose a novel adaptive empirical Bayesian (AEB) method for sparse d...
In this study, the applicability of generalized polynomial chaos (gPC)
e...
In many areas of science and engineering, discovering the governing
diff...
We present efficient deep learning techniques for approximating flow and...
The derivation of physical laws is a dominant topic in scientific resear...