Jekyll2026-09-14T16:58:05+00:00https://jaehwanjeon.github.io/feed.xmlJaehwan Jeon’s Research WebsiteJaehwan's academic portfolioJaehwan Jeonjaehwan.jeon@utoronto.ca[May 2026] Postdoctoral Researcher at the University of Toronto2026-05-01T00:00:00+00:002026-05-01T00:00:00+00:00https://jaehwanjeon.github.io/posts/2026/05/postdoc-uoftI joined the Department of Civil and Mineral Engineering at the University of Toronto as a Postdoctoral Researcher (May 2026 – present).

University of Toronto

]]>Jaehwan Jeonjaehwan.jeon@utoronto.ca[New Paper] Ensemble-based Uncertainty Quantification and Decomposition of Probabilistic Surrogate Models Using Bayesian Neural Networks2026-03-13T00:00:00+00:002026-03-13T00:00:00+00:00https://jaehwanjeon.github.io/posts/2026/03/strusafe-ensemble-uqOur paper “Ensemble-based Uncertainty Quantification and Decomposition of Probabilistic Surrogate Models Using Bayesian Neural Networks” has been published in Structural Safety (available online 13 March 2026).

The study presents a new framework for decomposing predictive uncertainty in ensembles of Bayesian neural networks, systematically separating irreducible randomness from reducible model-form and data-induced uncertainties.

Ensemble-based uncertainty quantification and decomposition

Citation: Jeon, J., Song, J., & Kwon, O. (2026). “Ensemble-based Uncertainty Quantification and Decomposition of Probabilistic Surrogate Models Using Bayesian Neural Networks.” Structural Safety. https://doi.org/10.1016/j.strusafe.2026.102698

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]]>Jaehwan Jeonjaehwan.jeon@utoronto.ca[Mar. 2026] Postdoctoral Researcher at Seoul National University2026-03-01T00:00:00+00:002026-03-01T00:00:00+00:00https://jaehwanjeon.github.io/posts/2026/03/postdoc-snuI joined the Institute of Construction and Environmental Engineering at Seoul National University as a Postdoctoral Researcher (Mar. 2026 – Apr. 2026).

]]>Jaehwan Jeonjaehwan.jeon@utoronto.ca[New Paper] Unified Hysteresis Modeling via Physics-based Deep Learning and Data Augmentation2026-01-01T00:00:00+00:002026-01-01T00:00:00+00:00https://jaehwanjeon.github.io/posts/2026/01/eesd-unified-hysteresisOur paper “Unified Hysteresis Modeling via Physics-based Deep Learning and Data Augmentation” has been published in Earthquake Engineering & Structural Dynamics.

The paper proposes a unified hysteresis modeling framework based on deep learning, leveraging a physics-encoded architecture and physics-informed loss functions to capture complex nonlinearities under stochastic excitations.

Citation: Jeon, J., Kwon, O., & Song, J. (2026). “Unified Hysteresis Modeling via Physics-based Deep Learning and Data Augmentation.” Earthquake Engineering & Structural Dynamics. DOI: 10.1002/eqe.70081

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]]>Jaehwan Jeonjaehwan.jeon@utoronto.ca[New Paper] Neural Network–Augmented Physics Models Using Modal Truncation for Dynamic MDOF Systems under Response-Dependent Forces2025-01-01T00:00:00+00:002025-01-01T00:00:00+00:00https://jaehwanjeon.github.io/posts/2025/01/jem-nn-augmented-physicsOur paper “Neural Network–Augmented Physics Models Using Modal Truncation for Dynamic MDOF Systems under Response-Dependent Forces” has been published in the Journal of Engineering Mechanics.

The work develops a neural network–augmented physics model for multi-degree-of-freedom systems under response-dependent forces using modal truncation.

Citation: Jaehwan Jeon and Junho Song (2025). “Neural Network–Augmented Physics Models Using Modal Truncation for Dynamic MDOF Systems under Response-Dependent Forces.” Journal of Engineering Mechanics.

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