DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks

Christian Moya, Guang Lin

AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems

Jiyong Kwon, Ujin Jeon, Sooji Lee +1

Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition

Changhong Mou, Binghang Lu, Guang Lin

MFPC-Net: Multi-fidelity Physics-Constrained Neural Process

Yating Wang, Guang Lin

A POD-DeepONet Framework for Forward and Inverse Design of 2D Photonic Crystals

Yueqi Wang, Guanglian Li, Guang Lin

Latent Transformations for Object View Points Synthesis

Sangpil Kim, Nick Winovich, Guang Lin +1

A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients

Amirhossein Mollaali, Izzet Sahin, Iqrar Raza +3

Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems

Hugo Esquivel, Arun Prakash, Guang Lin

RotEqNet: Rotation-Equivariant Network for Fluid Systems with Symmetric High-Order Tensors

Liyao Gao, Yifan Du, Hongshan Li +1

LLM Safety Alignment is Divergence Estimation in Disguise

Rajdeep Haldar, Ziyi Wang, Qifan Song +2

Coefficient-to-Basis Network: A Fine-Tunable Operator Learning Framework for Inverse Problems with Adaptive Discretizations and Theoretical Guarantees

Zecheng Zhang, Hao Liu, Wenjing Liao +1

Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo

Haoyang Zheng, Wei Deng, Christian Moya +1

A Review of AI-Driven Approaches for Nanoscale Heat Conduction and Radiation

Ziqi Guo, Daniel Carne, Krutarth Khot +3

On Convergence of Federated Averaging Langevin Dynamics

Wei Deng, Qian Zhang, Yi-An Ma +2

The Effect of Nonlinearity in Hybrid KMC-Continuum models

Ariel Balter, Guang Lin, Alexandre M. Tartakovsky

DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories

Christian Moya, Shiqi Zhang, Meng Yue +1

Energy-Dissipative Evolutionary Kolmogorov-Arnold Networks for Complex PDE Systems

Guang Lin, Changhong Mou, Jiahao Zhang

Weak-Form Evolutionary Kolmogorov-Arnold Networks for Solving Partial Differential Equations

Bongseok Kim, Jiahao Zhang, Guang Lin

Flow-driven spectral chaos (FSC) method for simulating long-time dynamics of arbitrary-order non-linear stochastic dynamical systems

Hugo Esquivel, Arun Prakash, Guang Lin

DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving

Wei Deng, Junwei Pan, Tian Zhou +3

D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators

Zecheng Zhang, Christian Moya, Lu Lu +2

Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling

Guang Lin, Shikui Tu, Lei Xu

Large Language Model Sentinel: LLM Agent for Adversarial Purification

Guang Lin, Toshihisa Tanaka, Qibin Zhao

Trimmed Ensemble Kalman Filter for Nonlinear and Non-Gaussian Data Assimilation Problems

Weixuan Li, W. Steven Rosenthal, Guang Lin

Gaussian Process Assisted Active Learning of Physical Laws

Jiuhai Chen, Lulu Kang, Guang Lin

Active operator learning with predictive uncertainty quantification for partial differential equations

Nick Winovich, Mitchell Daneker, Lu Lu +1

Gaussian process surrogates for failure detection: a Bayesian experimental design approach

Hongqiao Wang, Guang Lin, Jinglai Li

ATLAS: A Multi-LLM Training Framework for EvoDPO with Adaptive Reference Evolution

Ujin Jeon, Jiyong Kwon, Madison Ann Sullivan +2

Multi-Fidelity Gaussian Process based Empirical Potential Development for Si:H Nanowires

Moonseop Kim, Huayi Yin, Guang Lin

Some Best Practices in Operator Learning

Dustin Enyeart, Guang Lin

Bayesian data-driven discovery of partial differential equations with variable coefficients

Aoxue Chen, Yifan Du, Liyao Mars Gao +1

Numerical Solution of 3D Poisson-Nernst-Planck Equations Coupled with Classical Density Functional Theory for Modeling Ion and Electron Transport in a Confined Environment

Da Meng, Bin Zheng, Guang Lin +1

A Fast-Convergence Resolution of the Stochastic Eigenproblem Using Halley's Method and the Spectral-Chaos Approach

Hugo Esquivel, Kabir Oluwatobi Idowu, Guang Lin

DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning

Zecheng Zhang, Christian Moya, Lu Lu +2

An adaptive Hessian approximated stochastic gradient MCMC method

Yating Wang, Wei Deng, Guang Lin

Parallel and Interacting Stochastic Approximation Annealing algorithms for global optimisation

Georgios Karagiannis, Bledar A. Konomi, Guang Lin +1

iPINNER: An Iterative Physics-Informed Neural Network with Ensemble Kalman Filter

Binghang Lu, Changhong Mou, Guang Lin

An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method

Jiahao Zhang, Christian Moya, Guang Lin

Uncertainty quantification of thermal conductivities from equilibrium molecular dynamics simulations

Zuyuan Wang, Salar Safarkhani, Guang Lin +1

Training-Free Global Geometric Association for 4D LiDAR Panoptic Segmentation

Gyeongrok Oh, Youngdong Jang, Jonghyun Choi +3

Loss Terms and Operator Forms of Koopman Autoencoders

Dustin Enyeart, Guang Lin

Interacting Contour Stochastic Gradient Langevin Dynamics

Wei Deng, Siqi Liang, Botao Hao +2

Bayesian inverse regression for dimension reduction with small datasets

Xin Cai, Guang Lin, Jinglai Li

A second-order difference scheme for the time fractional substantial diffusion equation

Zhaopeng Hao, Wanrong Cao, Guang Lin

RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification

Jiahao Zhang, Shiqi Zhang, Guang Lin

LegONet: Plug-and-Play Structure-Preserving Neural Operator Blocks for Compositional PDE Learning

Jiahao Zhang, Yueqi Wang, Guang Lin

SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations

Sheng Zhang, Guang Lin

An Element-wise RSAV Algorithm for Unconstrained Optimization Problems

Shiheng Zhang, Jiahao Zhang, Jie Shen +1

Sampling-accelerated First-principles Prediction of Phonon Scattering Rates for Converged Thermal Conductivity and Radiative Properties

Ziqi Guo, Zherui Han, Dudong Feng +2

Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems with a Kalman-Inspired Proposal Distribution

Jiangjiang Zhang, Jasper A. Vrugt, Xiaoqing Shi +3

Fast Replica Exchange Stochastic Gradient Langevin Dynamics

Guanxun Li, Guang Lin, Zecheng Zhang +1

Morephy-Net: An Evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed Neural Operator Learning Networks

Binghang Lu, Changhong Mou, Guang Lin

Physics Informed Constrained Learning of Dynamics from Static Data

Pengtao Dang, Tingbo Guo, Melissa Fishel +4

Reinforcement Learning for Traffic Control with Adaptive Horizon

Wentao Chen, Tehuan Chen, Guang Lin

DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems

Yixuan Sun, Christian Moya, Guang Lin +1

DiLO: Decoupling Generative Priors and Neural Operators via Diffusion Latent Optimization for Inverse Problems

Haibo Liu, Guang Lin

Federated X-Armed Bandit

Wenjie Li, Qifan Song, Jean Honorio +1

Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs

Guang Lin, Christian Moya, Zecheng Zhang

Fluorescence Correlation Spectroscopy and Nonlinear Stochastic Reaction-Diffusion

Mauricio J. Del Razo, Wenxiao Pan, Hong Qian +1

Low-Rank Evolutionary Deep Neural Networks via Adaptive Tangent-Space Reduction

Jiahao Zhang, Shiheng Zhang, Guang Lin

Ultra Fast PDE Solving via Physics Guided Few-step Diffusion

Cindy Xiangrui Kong, Yueqi Wang, Haoyang Zheng +2

HEI: hybrid explicit-implicit learning for multiscale problems

Yalchin Efendiev, Wing Tat Leung, Guang Lin +1

Block Triangular Preconditioning for Stochastic Galerkin Method

Bin Zheng, Guang Lin, Jinchao Xu

Numerical Stability for Differential Equations with Memory

Guihong Wang, Yuqing Li, Tao Luo +3

A consistent and conservative volume distribution algorithm and its applications to multiphase flows using Phase-Field models

Ziyang Huang, Guang Lin, Arezoo M. Ardekani

Spectral Anatomy of Quantum Gaussian Process Kernels

Jian Xu, Chao Li, Guang Lin +4

Exploring Non-Convex Discrete Energy Landscapes: An Efficient Langevin-Like Sampler with Replica Exchange

Haoyang Zheng, Hengrong Du, Ruqi Zhang +1

Jeffreys Flow: Robust Boltzmann Generators for Rare Event Sampling via Parallel Tempering Distillation

Guang Lin, Christian Moya, Di Qi +1

Enhancing Sparsity of Hermite Polynomial Expansions by Iterative Rotations

Xiu Yang, Huan Lei, Nathan A. Baker +1

LLM Reasoning Engine: Specialized Training for Enhanced Mathematical Reasoning

Shuguang Chen, Guang Lin

On Learning the Dynamical Response of Nonlinear Control Systems with Deep Operator Networks

Guang Lin, Christian Moya, Zecheng Zhang

Geometry-aware LegONet for PDE Learning on Arbitrary Domains

Jiahao Zhang, Yueqi Wang, Guang Lin

Turbulence Generation from a stochastic wavelet model

Yifan Du, Guang Lin

Muon-OGD: Muon-based Spectral Orthogonal Gradient Projection for LLM Continual Learning

Binghang Lu, Zheyuan Deng, Runyu Zhang +6

Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning

Binghang Lu, Jiahao Zhang, Guang Lin

Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks

Christian Moya, Amirhossein Mollaali, Zecheng Zhang +2

glassoformer: a query-sparse transformer for post-fault power grid voltage prediction

Yunling Zheng, Carson Hu, Guang Lin +3

Adversarial Autoencoders in Operator Learning

Dustin Enyeart, Guang Lin

Predicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks

Yixuan Sun, Imad Hanhan, Michael D. Sangid +1

Restoring the Discontinuous Heat Equation Source Using Sparse Boundary Data and Dynamic Sensors

Guang Lin, Na Ou, Zecheng Zhang +1

Federated Online Sparse Decision Making

Chi-Hua Wang, Wenjie Li, Guang Cheng +1

Comparative Study of Clustering Techniques for Real-Time Dynamic Model Reduction

Emilie Purvine, Eduardo Cotilla-Sanchez, Mahantesh Halappanavar +4

Data-driven Feynman-Kac Discovery with Applications to Prediction and Data Generation

Qi Feng, Guang Lin, Purav Matlia +1

Multi-Subdomain Adversarial Network for Cross-Subject EEG-based Emotion Recognition

Guang Lin, Jianhai Zhang

PO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational Structure

Junyi Wu, Guang Lin

On the Bayesian calibration of expensive computer models with input dependent parameters

Georgios Karagiannis, Bledar A. Konomi, Guang Lin

Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective

Weixin Wang, Haoyang Zheng, Guang Lin +2

A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions

Wei Deng, Guang Lin, Faming Liang

Generalized Discrete Diffusion with Self-Correction

Linxuan Wang, Ziyi Wang, Yikun Bai +3

Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL

Junyi Wu, Weijian Luo, Haoyang Zheng +2

Task-tailored Pre-processing: Fair Downstream Supervised Learning

Jinwon Sohn, Guang Lin, Qifan Song

Theoretical and numerical studies of inverse source problem for the linear parabolic equation with sparse boundary measurements

Guang Lin, Zecheng Zhang, Zhidong Zhang

A consistent hierarchy of generalized kinetic equation approximations to the chemical master equation applied to surface catalysis

Gregory Herschlag, Sorin Mitran, Guang Lin

Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation

Yan Xiang, Yu-Hang Tang, Zheng Gong +4

MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems

Jiahao Zhang, Shiqi Zhang, Guang Lin

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations

Young Hyun Cho, Franz Stoll, Will Wei Sun +2

A consistent and conservative Phase-Field model for thermo-gas-liquid-solid flows including liquid-solid phase change

Ziyang Huang, Guang Lin, Arezoo M. Ardekani

Efficient Deep Learning Techniques for Multiphase Flow Simulation in Heterogeneous Porous Media

Yating Wang, Guang Lin

Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility

Rajdeep Haldar, Yue Xing, Qifan Song +1

SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups

Yikun Bai, Binghang Lu, Yikai Liu +7

The paper presents SE(3)-MeanFlow, a generative model that creates protein backbone structures directly on the SE(3) Lie group using a few inference steps, avoiding costly ODE inte…