Selected work

Representative Papers

Within-fire estimates of biomass loss across national forests of the US West Coast: inventory-informed inference with uncertainty

Oct 04, 2026

This study addresses the lack of direct biomass change quantification and uncertainty characterization in wildfire impact assessments. By integrating U.S. Forest Inventory and Analysis (FIA) data with 30-meter resolution remote sensing imagery of tree canopy cover, we developed a Bayesian spatiotemporal model to estimate post-fire aboveground biomass loss across national forests on the U.S. West Coast. Incorporating inventory-based priors strengthened inference, enabling pixel-level spatial heterogeneity capture and rigorous uncertainty quantification. Results indicate cumulative biomass losses of 95.3 million metric tons and 2.39 million hectares of forest mortality within the study area, revealing pronounced spatial differentiation. These findings establish a novel paradigm for fine-grained fire ecology assessments.

0 citationsRead paper

Adapting neural operators for mechanics decisions under changing operating conditions

Sep 27, 2026

This study addresses the accuracy degradation and insufficient decision reliability of neural operators when extrapolating beyond their training conditions. We propose an adaptive updating framework leveraging high-fidelity finite element data, which periodically fine-tunes the neural operator using online-acquired loading paths and integrates an empirical error estimator to optimize mechanical command selection. Experiments demonstrate that minimal data suffices to substantially restore predictive accuracy and improve command decisions. Furthermore, this work reveals a discrepancy between enhanced forward prediction accuracy and decision confidence, confirming that prediction errors must be independently evaluated to ensure overall system reliability.

0 citationsRead paper

Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation

Aug 12, 2026

This study addresses the challenge of complex and evolving cyberattacks in cloud environments by proposing an adaptive dynamic defense framework based on reinforcement learning. The work introduces, for the first time, a Deep Q-Network (DQN) into cloud security to establish an end-to-end intelligent intrusion detection and automated response loop. Leveraging feature engineering and data preprocessing, the model is trained on the CICIDS2017 dataset and validated on UNSW-NB15. Experimental results demonstrate that the system achieves 99.72% accuracy, a 99.66% F1-score, and a 0.999 ROC-AUC under previously unseen and evolving attacks, with a false positive rate of only 0.31%, an average detection latency of 15 ms, and an attack mitigation rate of 99.54%, thereby significantly enhancing real-time performance and generalization capability.

0 citationsRead paper

Model-Informed Joint Material-Structural Optimization of Hard-Magnetic Soft Materials

Jul 15, 2026

This study addresses the challenges in predicting deformation and achieving co-optimized design of hard-magnetic soft materials under magnetic actuation by proposing a unified effective shear modulus framework. This framework integrates classical inclusion theory, the Hill self-consistent model, and constrained kinematic relations, and employs an experimentally calibrated Mooney strain energy function to formulate a multiphysics constitutive model. Building upon this foundation, a material–structure concurrent topology optimization method is developed to simultaneously tailor structural density, magnetic particle distribution, and remanent magnetization orientation. The proposed framework successfully generates non-intuitive designs capable of achieving prescribed deformations—such as rotation, translation, and recovery—demonstrating its versatility and precise controllability across single- and multi-loading scenarios and diverse design objectives.

0 citationsRead paper

Koopman Operator Identification of Model Parameter Trajectories for Temporal Domain Generalization (KOMET)

Mar 27, 2026

This work addresses the performance degradation of machine learning models in non-stationary environments caused by temporal domain drift by proposing a model-agnostic, zero-retraining adaptive framework. The approach models the sequence of model parameters as a trajectory of a nonlinear dynamical system and identifies its linear Koopman operator using extended dynamic mode decomposition (EDMD) with a Fourier-augmented observation dictionary. Leveraging a warm-start training protocol, the framework autonomously predicts future parameter trajectories without requiring future labels, enabling efficient adaptation. Moreover, it uncovers an interpretable dynamical structure underlying decision boundary drift. Evaluated across six datasets, the method achieves average accuracies between 0.981 and 1.000 over 100 future timesteps, demonstrating robustness and effectiveness under diverse distribution shift scenarios.

0 citationsRead paper

Recent publications

Latest Papers

Within-fire estimates of biomass loss across national forests of the US West Coast: inventory-informed inference with uncertainty

Oct 04, 2026

This study addresses the lack of direct biomass change quantification and uncertainty characterization in wildfire impact assessments. By integrating U.S. Forest Inventory and Analysis (FIA) data with 30-meter resolution remote sensing imagery of tree canopy cover, we developed a Bayesian spatiotemporal model to estimate post-fire aboveground biomass loss across national forests on the U.S. West Coast. Incorporating inventory-based priors strengthened inference, enabling pixel-level spatial heterogeneity capture and rigorous uncertainty quantification. Results indicate cumulative biomass losses of 95.3 million metric tons and 2.39 million hectares of forest mortality within the study area, revealing pronounced spatial differentiation. These findings establish a novel paradigm for fine-grained fire ecology assessments.

0 citationsRead paper

Adapting neural operators for mechanics decisions under changing operating conditions

Sep 27, 2026

This study addresses the accuracy degradation and insufficient decision reliability of neural operators when extrapolating beyond their training conditions. We propose an adaptive updating framework leveraging high-fidelity finite element data, which periodically fine-tunes the neural operator using online-acquired loading paths and integrates an empirical error estimator to optimize mechanical command selection. Experiments demonstrate that minimal data suffices to substantially restore predictive accuracy and improve command decisions. Furthermore, this work reveals a discrepancy between enhanced forward prediction accuracy and decision confidence, confirming that prediction errors must be independently evaluated to ensure overall system reliability.

0 citationsRead paper

Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation

Aug 12, 2026

This study addresses the challenge of complex and evolving cyberattacks in cloud environments by proposing an adaptive dynamic defense framework based on reinforcement learning. The work introduces, for the first time, a Deep Q-Network (DQN) into cloud security to establish an end-to-end intelligent intrusion detection and automated response loop. Leveraging feature engineering and data preprocessing, the model is trained on the CICIDS2017 dataset and validated on UNSW-NB15. Experimental results demonstrate that the system achieves 99.72% accuracy, a 99.66% F1-score, and a 0.999 ROC-AUC under previously unseen and evolving attacks, with a false positive rate of only 0.31%, an average detection latency of 15 ms, and an attack mitigation rate of 99.54%, thereby significantly enhancing real-time performance and generalization capability.

0 citationsRead paper

Model-Informed Joint Material-Structural Optimization of Hard-Magnetic Soft Materials

Jul 15, 2026

This study addresses the challenges in predicting deformation and achieving co-optimized design of hard-magnetic soft materials under magnetic actuation by proposing a unified effective shear modulus framework. This framework integrates classical inclusion theory, the Hill self-consistent model, and constrained kinematic relations, and employs an experimentally calibrated Mooney strain energy function to formulate a multiphysics constitutive model. Building upon this foundation, a material–structure concurrent topology optimization method is developed to simultaneously tailor structural density, magnetic particle distribution, and remanent magnetization orientation. The proposed framework successfully generates non-intuitive designs capable of achieving prescribed deformations—such as rotation, translation, and recovery—demonstrating its versatility and precise controllability across single- and multi-loading scenarios and diverse design objectives.

0 citationsRead paper

Koopman Operator Identification of Model Parameter Trajectories for Temporal Domain Generalization (KOMET)

Mar 27, 2026

This work addresses the performance degradation of machine learning models in non-stationary environments caused by temporal domain drift by proposing a model-agnostic, zero-retraining adaptive framework. The approach models the sequence of model parameters as a trajectory of a nonlinear dynamical system and identifies its linear Koopman operator using extended dynamic mode decomposition (EDMD) with a Fourier-augmented observation dictionary. Leveraging a warm-start training protocol, the framework autonomously predicts future parameter trajectories without requiring future labels, enabling efficient adaptation. Moreover, it uncovers an interpretable dynamical structure underlying decision boundary drift. Evaluated across six datasets, the method achieves average accuracies between 0.981 and 1.000 over 100 future timesteps, demonstrating robustness and effectiveness under diverse distribution shift scenarios.

0 citationsRead paper