BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//pretalx.com//juliacon-2026//speaker//C7BG78 BEGIN:VTIMEZONE TZID:CET BEGIN:STANDARD DTSTART:20001029T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-juliacon-2026-N7CSPV@pretalx.com DTSTART;TZID=CET:20260812T143000 DTEND;TZID=CET:20260812T144500 DESCRIPTION:Structured light in interaction with matter has been of interes t\, particularly as it relates to the production of high intensity gamma b eams. In our package\, [ElectronDynamicsModels.jl]([url](https://github.co m/SebastianM-C/ElectronDynamicsModels.jl))\, we developed a way to efficie ntly compute the radiated field resulting from the scattering of a Laguerr e-Gauss laser beam off a thin sheet of electrons. The electrons are repres ented as relativistic classical particles whose motion is integrated using _DifferentialEquations.jl_. _ModellingToolkit.jl_ was used to formulate the model\, allowing us to take advantage of its compiler to generate effi cient _Julia_ code. Moreover\, this also enables an easy scaling to parall el ensemble simulations on the CPU and GPU. Besides performance\, this app roach is also useful for enabling higher precision computations\, which na turally leverage _Julia_'s multiple dispatch. Once the trajectories are kn own\, the far electromagnetic field can be computed over a grid of pixels from the Lienardt-Wiechert potentials\, and finally we compute their Fouri er transform. DTSTAMP:20260502T104015Z LOCATION:Room 5 SUMMARY:A purely numeric approach to the nonlinear coherent Thomson scatter ing by structured light. - Petru-Vlad TOMA\, Sebastian Micluța-Câmpeanu URL:https://pretalx.com/juliacon-2026/talk/N7CSPV/ END:VEVENT BEGIN:VEVENT UID:pretalx-juliacon-2026-YQXSKZ@pretalx.com DTSTART;TZID=CET:20260812T161500 DTEND;TZID=CET:20260812T163000 DESCRIPTION:In this talk we will present how DyadModelOptimizer is solving free final time problems using ModelingToolkit\, BoundaryValueDiffEq and O ptimizationMadNLP\, showing the full julia stack that powers the Dyad anal yses. Specifically we examine the solution in the context of minimum lap t ime optimization for a race car. The solution assumes a continuous lap opt imizing throttle and braking under dynamical constraints.  We formulate t he problem as a nonlinear optimal control problem with free terminal time\ , where the objective is to minimize total lap time subject to coupled veh icle dynamics\, tire force limits\, and path constraints along a prescribe d track centerline. The vehicle model captures longitudinal and lateral dy namics\, load transfer effects\, and tire saturation through nonlinear alg ebraic relationships\, resulting in a differential-algebraic system expres sed symbolically. The talk walks through the entire process end to end: bu ilding the symbolic model\, converting it into a boundary value formulatio n\, choosing a discretization strategy\, assembling the nonlinear program\ , and configuring the solver. We will also share practical lessons on mesh refinement\, scaling for numerical stability\, and what solve times and c onvergence actually look like in practice. DTSTAMP:20260502T104015Z LOCATION:Room 6 SUMMARY:Optimizing race car track times in Dyad - Sebastian Micluța-Câmpe anu\, Rajeev Voleti URL:https://pretalx.com/juliacon-2026/talk/YQXSKZ/ END:VEVENT BEGIN:VEVENT UID:pretalx-juliacon-2026-SFWUKP@pretalx.com DTSTART;TZID=CET:20260814T100000 DTEND;TZID=CET:20260814T103000 DESCRIPTION:Real-time adaptive experimental design for ODE models is hard: each step requires costly posterior inference and optimization. We train a neural network policy offline to amortize this cost. The Julia SciML stac k makes this practical: Enzyme.jl differentiates through ODEs\, Lux.jl def ines the policy network\, and Reactant.jl compiles everything to a single GPU program. On a bioreactor benchmark\, the learned adaptive policy beats Bayesian D-optimal static designs with a 99.5% win rate. DTSTAMP:20260502T104015Z LOCATION:Room 1 SUMMARY:Deep Adaptive Experimental Design for SciML - Arno Strouwen\, Sebas tian Micluța-Câmpeanu URL:https://pretalx.com/juliacon-2026/talk/SFWUKP/ END:VEVENT BEGIN:VEVENT UID:pretalx-juliacon-2026-FLU7MM@pretalx.com DTSTART;TZID=CET:20260814T100000 DTEND;TZID=CET:20260814T101500 DESCRIPTION:The Dyad platform allows engineers to leverage the power of Jul ia and SciML via a graphical system modeling environment. The models crea ted by engineers are translated into Julia and harness Julia's just-in-tim e compilation along with ModelingToolkit's symbolic manipulation capabilit ies to provide world class simulation performance. But what happens when you want to integrate these models into engineering workflows or wish to l everage the symbolic representations in different ways? In this talk\, we 'll describe Dyad analyses and how they provide a gateway to the expansive Julia ecosystem. DTSTAMP:20260502T104015Z LOCATION:Room 4 SUMMARY:Dyad Analyses: Designing Engineering Workflows with Julia - Michael Tiller\, Sebastian Micluța-Câmpeanu URL:https://pretalx.com/juliacon-2026/talk/FLU7MM/ END:VEVENT END:VCALENDAR