# A Novel Deep Learning Approach for Emulating Computationally Expensive Postfire Debris Flows

arXiv: https://arxiv.org/abs/2504.07736 · HTML: https://mlsanity.com/paper/2504.07736/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 49 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows](https://mlsanity.com/paper/1810.08217/) (2018)
- [Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling](https://mlsanity.com/paper/1710.11431/) (2017)
- [Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification](https://mlsanity.com/paper/1802.00850/) (2018)
- [U-net architectures for fast prediction of incompressible laminar flows](https://mlsanity.com/paper/1910.13532/) (2019)
- [Simulating Surface Wave Dynamics with Convolutional Networks](https://mlsanity.com/paper/2012.00718/) (2020)

## Abstract (arXiv)

Traditional physics-based models of geophysical flows, such as debris flows and landslides that pose significant risks to human lives and infrastructure are computationally expensive, limiting their utility for large-scale parameter sweeps, uncertainty quantification, inversions or real-time applications. This study presents an efficient alternative, a deep learning-based surrogate model built using a modified U-Net architecture to predict the dynamics of runoff-generated debris flows across diverse terrain based on data from physics based simulations. The study area is divided into smaller patches for localized predictions using a patch-predict-stitch methodology (complemented by limited global data to accelerate training). The patches are then combined to reconstruct spatially continuous flow maps, ensuring scalability for large domains. To enable fast training using limited expensive simulations, the deep learning model was trained on data from an ensemble of physics based simulations using parameters generated via Latin Hypercube Sampling and validated on unseen parameter sets and terrain, achieving maximum pointwise errors below 10% and robust generalization. Uncertainty quantification using Monte Carlo methods are enabled using the validated surrogate, which can facilitate probabilistic hazard assessments. This study highlights the potential of deep learning surrogates as powerful tools for geophysical flow analysis, enabling computationally efficient and reliable probabilistic hazard map predictions.