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SUMMARY:GlissADe.jl: Differentiable Simulator for Geophysical Surface Flow
 s - Tanish Jain\, Alan Correa
DTSTART;TZID=Europe/Berlin:20260814T120000
DTEND;TZID=Europe/Berlin:20260814T121500
DTSTAMP:20260916T090755Z
UID:pretalx-juliacon-2026-A79CZS@pretalx.com
DESCRIPTION:Geophysical surface flow phenomena such as avalanches\, landsl
 ides\, and floods pose significant risks to infrastructure and human safet
 y. 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 simul
 ators (e.g. Openfoam-Avalanche\, Avaframe\, and r.Avaflow) are robust\; th
 ey lack the differentiability required to efficiently perform uncertainty 
 quantification tasks like sensitivity analysis\, parameter calibration\, e
 tc.\, or discover new constitutive relations from observed data. \n\nWe in
 troduce 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 upo
 n the mathematical framework of surface-aligned depth-integrated shallow w
 ater 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 f
 or sensitivity analysis with respect to geometry\, initial conditions (e.g
 .\, release height)\, and physical process parameters (e.g.\, friction coe
 fficients and bulk density).\n\nThis talk explores the software architectu
 re of GlissADe.jl\, addressing the challenges of maintaining numerical sta
 bility while ensuring compatibility with operations that typically pose di
 fficulties for AD\, but are essential for geophysical simulators. These op
 erations include differentiating through iterative time-stepping schemes\,
  handling non-smooth flux limiters\, and managing in-place memory mutation
 s. We demonstrate how this framework is used for topographic uncertainty q
 uantification. Finally\, we discuss the potential application of such diff
 erentiable simulators to solve inverse problems in geophysical flow.\n\n\n
 [1] [M. Rauter\, Ž. Tuković\, A finite area scheme for shallow granular 
 flows on three-dimensional surfaces\,\nComputers & Fluids\, Volume 166\, 2
 018\, Pages 184-199\, ISSN 0045-7930\,](https://doi.org/10.1016/j.compflui
 d.2018.02.017).
LOCATION:Alte Mensa — Atrium Maximum
URL:https://pretalx.com/juliacon-2026/talk/A79CZS/
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BEGIN:VEVENT
SUMMARY:Differentiable Modeling BoF: Discussion and Future Directions - Sa
 rah Williamson\, Alan Correa
DTSTART;TZID=Europe/Berlin:20260814T123000
DTEND;TZID=Europe/Berlin:20260814T130000
DTSTAMP:20260916T090755Z
UID:pretalx-juliacon-2026-KLDKCP@pretalx.com
DESCRIPTION:This BoF is a roundtable discussion on differentiable computat
 ional models and their applications\, open to anyone using or developing t
 hem in Julia or other languages. We'll discuss:\n\nExperiences\, challenge
 s\, and solutions from working with differentiable models\nWhether existin
 g differentiation packages provide straightforward explanations of their u
 ses and how to implement them in real work\nLimitations in current differe
 ntiation packages that future development could address\nThe potential for
  an org page to consolidate and showcase community efforts and best practi
 ces in this space
LOCATION:Alte Mensa — Atrium Maximum
URL:https://pretalx.com/juliacon-2026/talk/KLDKCP/
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