GlissADe.jl: Differentiable Simulator for Geophysical Surface Flows
Geophysical surface flow phenomena such as avalanches, landslides, and floods pose significant risks to infrastructure and human safety. Recently, surface flow simulators are used to predict flow dynamics, inundation zones and develop hazard maps, allowing for effective disaster management strategies and safer engineering designs. While existing simulators (e.g. Openfoam-Avalanche, Avaframe, and r.Avaflow) are robust; they lack the differentiability required to efficiently perform uncertainty quantification tasks like sensitivity analysis, parameter calibration, etc., or discover new constitutive relations from observed data.
We introduce GlissADe.jl, developed for differentiable Finite-Area-Method (FAM) simulations, which enables the integration of physical simulations into gradient-based workflows and scientific machine learning. It is built upon the mathematical framework of surface-aligned depth-integrated shallow water equations [1]. By leveraging Julia’s automatic differentiation (AD) ecosystem, including ForwardDiff.jl and Enzyme.jl, GlissADe.jl enables the direct computation of gradients across all model inputs. This allows for sensitivity analysis with respect to geometry, initial conditions (e.g., release height), and physical process parameters (e.g., friction coefficients and bulk density).
This talk explores the software architecture of GlissADe.jl, addressing the challenges of maintaining numerical stability while ensuring compatibility with operations that typically pose difficulties for AD, but are essential for geophysical simulators. These operations include differentiating through iterative time-stepping schemes, handling non-smooth flux limiters, and managing in-place memory mutations. We demonstrate how this framework is used for topographic uncertainty quantification. Finally, we discuss the potential application of such differentiable simulators to solve inverse problems in geophysical flow.
Tanish Jain is an amateur researcher and an alumnus from IIT Kharagpur. During his time at IIT, he developed a keen interest in the Finite-Area Method (FAM) and its applications in modeling avalanches and landslides. His current work leverages the Julia SciML ecosystem to explore topographic uncertainty and parameter calibration, aiming to provide researchers with more robust tools for hazard mitigation in mountainous regions. His open-source contributions and current work on GlissADe.jl can be found on his GitHub profile at @reckylurker.
I am an enthusiastic doctoral researcher working at the intersection of computational modeling, uncertainty quantification, and sustainable computing. My work focuses on developing robust and sustainable methods for high-dimensional uncertainty propagation and heterogeneous computational workflows in model-based engineering applications. I am also a passionate research software engineer who advocates for open science and actively contributes to collaborative open-source projects. My goal is to create innovative solutions that empower better decision-making in complex systems while ensuring our methodologies prioritize sustainability through responsible resource usage.