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  • Selected Preprints
  • Selected Publications
    • 2D-ProteinRAG: Mitigating Intent-Context Misalignment in Homology-Based Protein Question Answering
    • Adaptive multigrid strategy for geometry optimization of large-scale three dimensional molecular mechanics
    • An AI-Ready Fine-Tuning Framework for Accurate Machine-Learning Interatomic Potentials in Solid–Solid Battery Interfaces
    • Autonomous Knowledge Distillation for an AI-Ready Knowledge Layer
    • Configuration Selection and Matrix-Aware Optimization for Data-Efficient Atomistic Foundation Models
    • Construction and a Priori Analysis of a Linear Elasticity Enhanced Multiscale Coupling Method for a Defective Atomistic Chain
    • Evaluating Universal Interatomic Potentials for Metallic Systems: From Elastic Response to Defect Energetics and Ideal Strength
    • Flexible Boundary Sequential Coupling for Atomistic Simulation of Crystal Defects
    • Geometry-conditioned basin initialization for variational quantum eigensolvers in strongly correlated molecules
    • Higher-Order Boundary Conditions for Atomistic Dislocation Simulations
    • NanoTitan: An AI-Driven Memory-Efficient Platform for High-Performance Molecular Dynamics Simulation
    • TensorSketch-Compressed High-Order Atomic Cluster Expansion Potentials: Accuracy, Mechanics, and Vibrational Thermodynamics
    • A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
    • A Discrete-Time Random Feature Method for Nonlinear Evolution Equations with Implicit-Explicit Runge–Kutta Time Stepping
    • A Foundation Model for Atomistic Materials Chemistry
    • A Framework for a Generalisation Analysis of Machine-learned Interatomic Potentials
    • A Posteriori Analysis and Adaptive Algorithms for Blended Type Atomistic-to-Continuum Coupling with Higher-Order Finite Elements
    • A Posteriori Error Estimate and Adaptivity for QM/MM Models of Crystalline Defects
    • A Posteriori Error Estimates for Adaptive QM/MM Coupling Methods
    • A Priori Analysis of a Higher Order Nonlinear Elasticity Model for an Atomistic Chain with Periodic Boundary Condition
    • A Structure-Adaptive Random Feature Method for High-Dimensional Elliptic PDEs
    • A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs)
    • A Theoretical Case Study of the Generalisation of Machine-learned Potentials
    • Adaptive QM/MM Coupling for Crystalline Defects
    • Amplitude Expansion Phase Field Crystal (APFC) Modeling based Efficient Dislocation Simulations using Fourier Pseudospectral Method
    • An Atomic Cluster Expansion Potential for Twisted Multilayer Graphene
    • Analysis of Hessian Variance Scaling for Local and Global Cost Functions in Variational Quantum Algorithms
    • Efficient a Posteriori Error Control of a Consistent Atomistic/Continuum Coupling Method for Two Dimensional Crystalline Defects
    • Elastic Far-field Decay from Dislocations in Multilattices
    • Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
    • Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
    • Formulation and Analysis of Blended Atomistic to Higher-Order Continuum Coupling Methods for Crystalline Defects
    • From Energy-Force Weighting to Primal-Dual Optimization of Machine-Learned Interatomic Potentials
    • Geometry-Preserving Nudged Elastic Band and Dimer Methods under Anisotropic Force Uncertainty
    • Higher Order Far-Field Boundary Conditions for Crystalline Defects
    • IADR: Interface-Augmented Neural Operator for Phase-Field Mean-Curvature Flow
    • LDD-RFM: Learnable Domain Decomposition for Random Feature Models via Variable Projection
    • Liquid Random Feature Methods for Time-Dependent Partial Differential Equations
    • Local Surrogates for Harmonic Vibrational Entropy in Multilattices
    • Many-Body Coarse-Grained Molecular Dynamics with the Atomic Cluster Expansion
    • Mathematical Modeling of Materials at the Atomic Scale
    • MeshAC: A 3D Mesh Generation and Adaptation Package for Multiscale Coupling Methods
    • MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction
    • Moment-Informed Force Rescaling for Random-Batch Langevin Dynamics
    • Optimal Sobolev Approximation by Deterministic and Random Shallow Sigmoidal Networks
    • QM/MM Methods for Crystalline Defects. Part 3: Machine-learned MM Models
    • Random Feature Kalman Filtering for Linear PDE Data Assimilation
    • Residual-Christoffel Sampling for Random Feature Collocation of Linear PDEs
    • Some Progress on Multi-scale Methods in Materials Modeling
    • Surrogate models for vibrational entropy based on a spatial decomposition
    • Trainability-Oriented Hybrid Quantum Regression via Geometric Preconditioning and Curriculum Optimization
    • Trainable Photonic Measurement for Physics-Informed PDE Learning
  • Talks & Conferences
    • Workshop on Mathematical Theory, Methods, and Applications in Materials Simulations (Tianyuan Mathematical Center in Science and Technology, TMCST)
    • Computational Multiscale Methods
    • 22nd Annual Conference of China Society for Industrial and Applied Mathematics (CSIAM 2024)
    • International Workshop on Data-driven Computational and Theoretical Materials Design (DCTMD)
    • Numerical Methods, Mathematical Modeling and Analysis in Materials Science
    • Machine Learning in Multiscale and Reduced Order Methods for the Simulation of Physical Systems (International Conference on Scientific Computation and Differential Equations, SciCADE 2024)
    • Advancing Molecular Simulations with Machine-Learned Interatomic Potentials
    • Advancing Molecular Simulations with Machine-Learned Interatomic Potentials
    • Using Uncertainty Quantification to Improve Learning in Atomistic Modeling (SIAM Conference on Mathematical Aspects of Materials Science)
    • Machine Learning Force Fields, Data-Driven Materials Informatics (Institute for Mathematical and Statistical Innovation)
    • Advancing Machine-Learned Interatomic Potentials: Enhancing Accuracy and Robustness in Materials Science Applications (Data Science Seminar)
    • Enhancing Machine-Learned Interatomic Potentials in Materials Science: Progressing from Accuracy to Robustness (Hot Topics in AI for Sciences)
    • Application of machine-learned interatomic potentials in atomic-scale simulations and beyond (Model Reduction and Simulation at Atomic Scale)
    • Analysis, Methods and Applications in Complex Materials
    • Mathematical Modeling, Analysis and Applications of Machine-Learned Interatomic Potentials
    • Atomic Cluster Expansion with and without Atoms
    • A Framework for a Generalisation Analysis of Machine-Learned Interatomic Potentials (Atomic Cluster Expansion (ACE) Seminar)
    • A Framework for a Generalisation Analysis of Machine-Learned Interatomic Potentials (online) (Computational Methods in Applied Mathematics, CMAM 2022)
    • Error Propagation of Machine-Learned Interatomic Potentials (MLIPs) (Machine-learned Interatomic Potentials Mini-workshop)
    • A Posteriori Error Estimates for Adaptive QM/MM Coupling Methods (Workshop on Computational Materials Science)
    • The Applications of Data-Driven Interatomic Potentials to QM/MM Coupling Methods (International Workshop on Mathematical Theory, Methods and Application in Materials Simulation)
    • Adaptive QM/MM Coupling Methods (Workshop on Materials Modeling, University of Warwick-Shanghai Jiao Tong University-University of British Columbia)
    • Some Recent Progress on Multiscale Coupling Methods (Top-notch Doctoral Seminar in Computational and Applied Mathematics)
  • Teaching

22nd Annual Conference of China Society for Industrial and Applied Mathematics (CSIAM 2024)

Oct 25, 2024·
Yangshuai Wang
Yangshuai Wang
conferences
Last updated on Oct 25, 2024
Yangshuai Wang
Authors
Yangshuai Wang
Peng Tsu Ann Assistant Professor (Visiting Fellow)
← Computational Multiscale Methods Apr 27, 2025
International Workshop on Data-driven Computational and Theoretical Materials Design (DCTMD) Oct 9, 2024 →

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