249 citations · 279 across the 40 of their papers we have counts for
43 papers
Optimal Sobolev Approximation by Deterministic and Random Shallow Sigmoidal Networks
Zhaohui Fu, Yangshuai Wang
Shallow networks with prescribed or randomly sampled hidden parameters are widely used as numerical trial spaces, yet their optimal Sobolev approximation power with standard smooth…
Efficient higher-order multi-scale method and its convergence estimate for dynamic nonlinear hygro-thermo-mechanical coupling problems of heterogeneous structures
Yifei Ding, Hao Dong, Jiale Linghu +1
This paper presents a novel higher-order multi-scale (HOMS) computational framework for efficient, high-accuracy, and low-cost simulation of nonlinear hygro-thermo-mechanical (H-T-…
A Structure-Adaptive Random Feature Method for High-Dimensional Elliptic PDEs
Jiale Linghu, Hao Dong, Yangshuai Wang
Random-feature methods reduce high-dimensional elliptic PDE collocation to linear coefficient problems, but full-dimensional trial spaces overlook lower-dimensional structure. We i…
Residual-Christoffel Sampling for Random Feature Collocation of Linear PDEs
Jiale Linghu, Yangshuai Wang
Random feature collocation fixes a randomly generated trial space and determines its coefficients from a linear least-squares system. Stability then depends on whether the sampled…
Trainable Photonic Measurement for Physics-Informed PDE Learning
Jiale Linghu, Hao Dong, Yangshuai Wang
Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement. However, its role in scientific machine learn…
Random-Feature Kalman Filtering for Linear PDE Data Assimilation
Xi'an Li, Jiale Linghu, Yangshuai Wang
Data assimilation for time-dependent partial differential equations (PDEs) requires Bayesian updates of an evolving field from streaming, sparse, and noisy observations, while keep…
Liquid Random Feature Methods for Time-Dependent Partial Differential Equations
Jiale Linghu, Yangshuai Wang
A central challenge in mesh-free space--time approximation for time-dependent partial differential equations is to represent evolving temporal scales while keeping residual minimiz…
Local Surrogates for Harmonic Vibrational Entropy in Multilattices
Tina Torabi, Jiale Linghu, Yangshuai Wang
Harmonic vibrational entropy is a key finite-temperature contribution to defect thermodynamics, but direct evaluation by dense Hessian diagonalization scales cubically with atom co…
Reliable Adaptive Stopping for Krylov-Shadow Quantum Fisher Information Estimation
Erjie Liu, Yangshuai Wang
Scalable quantum Fisher information (QFI) estimation becomes actionable when the numerical estimate is paired with a trustworthy stopping decision. Krylov-shadow QFI estimation has…
Geometry-Preserving Nudged Elastic Band and Dimer Methods under Anisotropic Force Uncertainty
Yifan Yu, Yangshuai Wang
The nudged elastic band (NEB) and Dimer methods are standard tools for computing minimum-energy paths and index-one saddle points in atomistic transition problems. They are increas…
Symmetry-Protected Basin Localization in Variational Quantum Eigensolvers
Yangshuai Wang
Variational quantum eigensolvers fail before optimization begins when strong correlation splits the molecular energy landscape into competing basins and the initial state selects a…
A Discrete-Time Random Feature Method for Nonlinear Evolution Equations with Implicit-Explicit Runge--Kutta Time Stepping
Haoran Zhou, Zhaohui Fu, Yangshuai Wang +1
We study a discrete-time random feature method for nonlinear, time-dependent partial differential equations. In contrast to continuous-time formulations that treat time as an addit…
Analysis of Hessian Scaling for Local and Global Costs in Variational Quantum Algorithm
Yihan Huang, Yangshuai Wang
Barren plateaus in variational quantum algorithms are typically described by gradient concentration at random initialization. In contrast, rigorous results for the Hessian, even at…
An AI-ready fine-tuning framework for accurate machine-learning interatomic potentials in solid-solid battery interfaces
Xiaoqing Liu, Xinyu Yu, Yangshuai Wang +6
Atomistic modeling of solid-solid battery interfaces is essential for understanding electro-chemo-mechanical coupling, but the complex interfacial chemistry and heterogeneous envir…
Trainability-Oriented Hybrid Quantum Regression via Geometric Preconditioning and Curriculum Optimization
Qingyu Meng, Yangshuai Wang
Quantum neural networks (QNNs) have attracted growing interest for scientific machine learning, yet in regression settings they often suffer from limited trainability under noisy g…
Beyond Adam: Disentangling Optimizer Effects in the Fine-Tuning of Atomistic Foundation Models
Xiaoqing Liu, Yangshuai Wang, Teng Zhao
Atomistic foundation models constitute a paradigm shift in computational materials science by providing universal machine-learned interatomic potentials with broad transferability…
MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction
Qinyi Zhang, Duanyu Feng, Ronghui Han +2
Simulating microstructure evolution (MicroEvo) is vital for materials design but demands high numerical accuracy, efficiency, and physical fidelity. Although recent studies on deep…
Higher-Order Boundary Conditions for Atomistic Dislocation Simulations
Xinyi Wei, Julian Braun, Yangshuai Wang +1
We present a higher-order boundary condition for atomistic simulations of dislocations that address the slow convergence of standard supercell methods. The method is based on a mul…
Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
Cheuk Hin Ho, Christoph Ortner, Yangshuai Wang
Reliable uncertainty quantification (UQ) is essential for developing machine-learned interatomic potentials (MLIPs) in predictive atomistic simulations. Conformal prediction (CP) i…
A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
Yifan Yu, Cheuk Hin Ho, Yangshuai Wang
Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving PDEs, yet existing uncertainty quantification (UQ) approaches for PINNs generally lack rig…
A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs)
Xiaoqing Liu, Kehan Zeng, Yangshuai Wang +1
Universal machine-learned interatomic potentials (U-MLIPs) have demonstrated effectiveness across diverse atomistic systems but often require fine-tuning for task-specific accuracy…
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
Xiaoqing Liu, Kehan Zeng, Zedong Luo +3
Universal machine-learned interatomic potentials (U-MLIPs) have demonstrated broad applicability across diverse atomistic systems but often require fine-tuning to achieve task-spec…
An Atomic Cluster Expansion Potential for Twisted Multilayer Graphene
Yangshuai Wang, Drake Clark, Sambit Das +5
Twisted multilayer graphene, characterized by its moiré patterns arising from inter-layer rotational misalignment, serves as a rich platform for exploring quantum phenomena. Machin…
Formulation and Analysis of Blended Atomistic to Higher-Order Continuum Coupling Methods for Crystalline Defects
Junfeng Lu, Hao Wang, Yangshuai Wang
Concurrent multiscale methods play an important role in modeling and simulating materials with defects, aiming to achieve the balance between accuracy and efficiency. Atomistic-to-…