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Blog Post number 1
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portfolio
Research on the use of YOLOv5 object detection algorithm in mask wearing recognition
Published:
YOLOv5-based detection system for real-time mask compliance monitoring.
NSGA-PINN: A multi-objective optimization method for physics-informed neural network training
Published:
Multi-objective optimization for PINNs using NSGA-II frameworks.
MoPINNEnKF: Iterative Model Inference using generic-PINN-based ensemble Kalman filter
Published:
Combining physics-informed neural networks with ensemble Kalman filters to perform robust and iterative model inference.
Fpinn-deeponet: An operator learning framework for multi-term time-fractional mixed diffusion-wave equations
Published:
A DeepONet-based operator learning framework for complex time-fractional PDEs.
Portfolio item number 1
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publications
Research on the Use of YOLOv5 Object Detection Algorithm in Mask Wearing Recognition
Published in World Scientific Research Journal, 2020
A YOLOv5-based real-time object detection system for recognizing mask-wearing behavior during the COVID-19 pandemic.
Recommended citation: Liu, Y., Lu, B. H., Peng, J., & Zhang, Z. (2020). "Research on the use of YOLOv5 object detection algorithm in mask wearing recognition." World Scientific Research Journal, 6(11), 276–284.
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
Published in Algorithms, 2023
A multi-objective evolutionary optimization framework (NSGA-II) for jointly minimizing data and physics residuals in PINNs.
Recommended citation: Lu, B., Moya, C., & Lin, G. (2023). "NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training." Algorithms, 16(4), 194.
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fPINN-DeepONet: A Physics-Informed Operator Learning Framework for Multi-Term Time-Fractional Mixed Diffusion–Wave Equations
Published in Journal of Computational Physics, 2025
A physics-informed DeepONet that learns the solution operator of multi-term time-fractional mixed diffusion–wave equations.
Recommended citation: Lu, B., Hao, Z.-P., Moya, C., & Lin, G. (2025). "fPINN-DeepONet: A physics-informed operator learning framework for multi-term time-fractional mixed diffusion–wave equations." Journal of Computational Physics, 538, 114184.
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MoPINNEnKF / iPINNER: Iterative Physics-Informed Neural Network with Ensemble Kalman Filter
Published in Journal of Computational Physics, 2025
An iterative PINN–EnKF framework that couples physics-informed networks with ensemble Kalman filtering for robust parameter and state estimation under uncertainty.
Recommended citation: Lu, B., Mou, C., & Lin, G. (2025). "iPINNER: An iterative physics-informed neural network with ensemble Kalman filter." Journal of Computational Physics, 114592.
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Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning
Published in arXiv preprint, 2026
Combines the Muon optimizer with spectral guidance to accelerate training of scientific machine-learning models.
Recommended citation: Lu, B., Zhang, J., & Lin, G. (2026). "Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning." arXiv:2602.16167.
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Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition
Published in arXiv preprint, 2026
A plug-and-play neural operator framework for infinite-dimensional functional nonlinear POD, enabling efficient reduced-order modeling of complex PDE systems.
Recommended citation: Mou, C., Lu, B., & Lin, G. (2026). "Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition." arXiv:2602.15632.
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Chain of Risk: Safety Failures in Large Reasoning Models and Mitigation via Adaptive Multi-Principle Steering
Published in arXiv preprint, 2026
Characterizes how chain-of-thought reasoning compounds safety failures in large reasoning models and proposes adaptive multi-principle steering as a mitigation.
Recommended citation: Li, X., Hou, J., Deng, Z., Zhang, Z., Li, T., Lu, B., Hu, B., Zhao, Y., & Hao, Y. (2026). "Chain of Risk: Safety Failures in Large Reasoning Models and Mitigation via Adaptive Multi-Principle Steering." arXiv:2605.05678.
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Muon-OGD: Muon-based Spectral Orthogonal Gradient Projection for LLM Continual Learning
Published in arXiv preprint, 2026
A spectral orthogonal-gradient-projection variant of the Muon optimizer that enables LLMs to acquire new tasks while preserving prior capabilities.
Recommended citation: Lu, B., Deng, Z., Zhang, R., Hu, B., Zhao, Y., Tian, Y., Mou, C., Lin, G., & Li, X. (2026). "Muon-OGD: Muon-based Spectral Orthogonal Gradient Projection for LLM Continual Learning." arXiv:2605.08949.
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AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training
Published in arXiv preprint, 2026
An adaptive optimizer that uses feedback linearization to enforce hard constraints during physics-informed neural network training.
Recommended citation: Lu, B., Zhang, R., Mou, C., Li, N., & Lin, G. (2026). "AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training." arXiv:2605.08408.
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Morphy-Net: Evolutionary Multi-Objective Optimization for Replica-Exchange-Based Physics-Informed Neural Operator Learning Networks
Published in Computer Methods in Applied Mechanics and Engineering (CMAME), 2026
Evolutionary multi-objective optimization combined with replica exchange for training physics-informed neural operator networks. Accepted by CMAME 2026.
Recommended citation: Lu, B., Mou, C., & Lin, G. (2026). "Morphy-Net: Evolutionary Multi-Objective Optimization for Replica-Exchange-Based Physics-Informed Neural Operator Learning Networks." Computer Methods in Applied Mechanics and Engineering (accepted).
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talks
Operator Learning Of Multi-Term Time-Fractional Partial Differential Equation By fPINN-DeepONet
Published:
🎥 Watch the talk on YouTube Talked about our paper “FPINN-deeponet: An operator learning framework for multi-term time-fractional mixed diffusion-wave equations”
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.