# Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling

arXiv: https://arxiv.org/abs/1909.08118 · HTML: https://mlsanity.com/paper/1909.08118/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 30 of 40)

- [PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs](https://mlsanity.com/paper/2106.14103/) (2021)
- [Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling](https://mlsanity.com/paper/2102.13156/) (2021)
- [ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation](https://mlsanity.com/paper/2312.01728/) (2023)
- [A novel meta-learning initialization method for physics-informed neural networks](https://mlsanity.com/paper/2107.10991/) (2021)
- [SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain](https://mlsanity.com/paper/2210.14044/) (2022)
- [Interpretation of Time-Series Deep Models: A Survey](https://mlsanity.com/paper/2305.14582/) (2023)
- [A Review of Physics-based Machine Learning in Civil Engineering](https://mlsanity.com/paper/2110.04600/) (2021)
- [Physics-informed Deep Super-resolution for Spatiotemporal Data](https://mlsanity.com/paper/2208.01462/) (2022)
- [Neural Extended Kalman Filters for Learning and Predicting Dynamics of Structural Systems](https://mlsanity.com/paper/2210.04165/) (2022)
- [A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic](https://mlsanity.com/paper/2204.06362/) (2022)
- [Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes](https://mlsanity.com/paper/2104.11009/) (2021)
- [Learning the solution operator of two-dimensional incompressible Navier-Stokes equations using physics-aware convolutional neural networks](https://mlsanity.com/paper/2308.02137/) (2023)
- [Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties](https://mlsanity.com/paper/2309.00305/) (2023)
- [Neural Operators for Stochastic Modeling of Nonlinear Structural System Response to Natural Hazards](https://mlsanity.com/paper/2502.11279/) (2025)
- [Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures](https://mlsanity.com/paper/2207.07883/) (2022)
- [Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach](https://mlsanity.com/paper/2404.07399/) (2024)
- [Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction](https://mlsanity.com/paper/2412.20962/) (2024)
- [Graph-Based Physics-Guided Urban PM2.5 Air Quality Imputation with Constrained Monitoring Data](https://mlsanity.com/paper/2506.06917/) (2025)
- [Data-driven Full-waveform Inversion Surrogate using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2105.00100/) (2021)
- [Physics-guided generative adversarial network to learn physical models](https://mlsanity.com/paper/2304.11488/) (2023)
- [Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory](https://mlsanity.com/paper/2405.12802/) (2024)
- [Deep learning-based modularized loading protocol for parameter estimation of Bouc-Wen class models](https://mlsanity.com/paper/2411.02776/) (2024)
- [Resolving Extreme Data Scarcity by Explicit Physics Integration: An Application to Groundwater Heat Transport](https://mlsanity.com/paper/2507.06062/) (2025)
- [FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers](https://mlsanity.com/paper/2607.18020/) (2026)
- [Semi-supervised physics guided deep learning framework for predicting the I-V characteristics of GAN HEMT](https://mlsanity.com/paper/2110.10724/) (2021)
- [On the Application of Data-Driven Deep Neural Networks in Linear and Nonlinear Structural Dynamics](https://mlsanity.com/paper/2111.02784/) (2021)
- [Visual design intuition: Predicting dynamic properties of beams from raw cross-section images](https://mlsanity.com/paper/2111.09701/) (2021)
- [Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net](https://mlsanity.com/paper/2206.01122/) (2022)
- [Hysteretic Behavior Simulation Based on Pyramid Neural Network:Principle, Network Architecture, Case Study and Explanation](https://mlsanity.com/paper/2206.03990/) (2022)
- [Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures](https://mlsanity.com/paper/2402.17992/) (2024)

## Abstract (arXiv)

Seismic events, among many other natural hazards, reduce due functionality and exacerbate vulnerability of in-service buildings. Accurate modeling and prediction of building's response subjected to earthquakes makes possible to evaluate building performance. To this end, we leverage the recent advances in deep learning and develop a physics-guided convolutional neural network (PhyCNN) framework for data-driven seismic response modeling and serviceability assessment of buildings. The proposed PhyCNN approach is capable of accurately predicting building's seismic response in a data-driven fashion without the need of a physics-based analytical/numerical model. The basic concept is to train a deep PhyCNN model based on available seismic input-output datasets (e.g., from simulation or sensing) and physics constraints. The trained PhyCNN can then used as a surrogate model for structural seismic response prediction. Available physics (e.g., the law of dynamics) can provide constraints to the network outputs, alleviate overfitting issues, reduce the need of big training datasets, and thus improve the robustness of the trained model for more reliable prediction. The trained surrogate model is then utilized for fragility analysis given certain limit state criteria (e.g., the serviceability state). In addition, an unsupervised learning algorithm based on K-means clustering is also proposed to partition the limited number of datasets to training, validation and prediction categories, so as to maximize the use of limited datasets. The performance of the proposed approach is demonstrated through three case studies including both numerical and experimental examples. Convincing results illustrate that the proposed PhyCNN paradigm outperforms conventional pure data-based neural networks.