Alan Correa
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.
Sessions
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.
[1] M. Rauter, Ž. Tuković, A finite area scheme for shallow granular flows on three-dimensional surfaces,
Computers & Fluids, Volume 166, 2018, Pages 184-199, ISSN 0045-7930,.
This BoF is a roundtable discussion on differentiable computational models and their applications, open to anyone using or developing them in Julia or other languages. We'll discuss:
Experiences, challenges, and solutions from working with differentiable models
Whether existing differentiation packages provide straightforward explanations of their uses and how to implement them in real work
Limitations in current differentiation packages that future development could address
The potential for an org page to consolidate and showcase community efforts and best practices in this space