James L. (Jim) Beck
George W. Housner Professor of Engineering and Applied Science, Emeritus
Research interests: probability logic, computational Bayesian statistics, computational stochastic dynamics, quantum stochastic mechanics, system reliability theory, Bayesian system identification, stochastic structural dynamics, stochastically robust structural control
Overview
Professor Beck focuses on the development of theory and algorithms for stochastic system modeling, uncertainty propagation and Bayesian updating of dynamic systems and networks based on sensor data, treating both modeling and excitation uncertainty. The primary computational tools are advanced stochastic simulation algorithms based on Markov chain Monte Carlo concepts. Some applications of current interest are stochastic predictions of the performance of structural systems under earthquakes, reliability assessment of technological networks, fast automated decision making for mitigation actions based on earthquake early warning systems, earthquake source inversions from seismic sensor networks, damage detection and assessment from structural sensor monitoring networks, Bayesian compressive sensing, and a stochastic mechanics approach to quantum mechanics.
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- Meng, Xianghao;Beck, James L. et al. (2025) Adaptive meta-learning stochastic gradient Hamiltonian Monte Carlo simulation for Bayesian updating of structural dynamic modelsComputer Methods in Applied Mechanics and Engineering
- Li, Qi;Gao, Jingze et al. (2023) Probabilistic outlier detection for robust regression modeling of structural response for high-speed railway track monitoringStructural Health Monitoring
- Xue, Shicheng;Zhou, Wensong et al. (2023) Damage localization and robust diagnostics in guided-wave testing using multitask complex hierarchical sparse Bayesian learningMechanical Systems and Signal Processing
- Beck, James L. (2023) Neo-classical Relativistic Mechanics Theory for Electrons that Exhibits Spin, Zitterbewegung, Dipole Moments, Wavefunctions and Dirac's Wave EquationFoundations of Physics
- Wang, Chenyue;Gao, Jingze et al. (2022) Robust sparse Bayesian learning for broad learning with application to high-speed railway track monitoringStructural Health Monitoring
- Beck, James L.;Bursi, Oreste S. et al. (2022) IntroductionComputer-Aided Civil and Infrastructure Engineering
- Beck, James L.;Pei, Jin-Song (2022) Demonstrating the power of extended Masing models for hysteresis through model equivalencies and numerical investigationNonlinear Dynamics
- Filippitzis, Filippos;Kohler, Monica D. et al. (2022) Sparse Bayesian learning for damage identification using nonlinear models: Application to weld fractures of steel-frame buildingsStructural Control and Health Monitoring
- Beck, James L.;Bursi, Oreste S. et al. (2021) IntroductionComputer-Aided Civil and Infrastructure Engineering
- Wang, Xiaoyou;Li, Lingfang et al. (2021) Sparse Bayesian factor analysis for structural damage detection under unknown environmental conditionsMechanical Systems and Signal Processing