The adoption of differentiable modeling and simulation has gained significant traction in computational science and engineering (CSE) workflows, driving advancements in optimization, sensitivity analysis, uncertainty quantification, model discovery, and more. This minisymposium brings together scientists, researchers, practitioners, students, and software developers who are either utilizing or developing differentiable computational models. We also welcome those seeking inspiration to integrate these methodologies into their own work. Our goal is to provide a platform for knowledge exchange and learning from each other's experiences—highlighting both success stories and challenges encountered along the way.
Differentiable computational models hold great promise for accelerating scientific discovery and research. However, their inherent complexity in development makes real-world applications challenging. The literature provides examples and projects that showcase effective use of differentiable programming via algorithmic differentiation (AD) in constructing these models. Nevertheless, achieving accurate and efficient outcomes often demands considerable expertise, effort, and rigorous testing. As a result, newcomers frequently face difficulties when navigating this intricate landscape.
Julia is not only a fast, scalable, flexible, and highly productive programming language; it has also cultivated a vibrant community of experts dedicated to building robust and efficient computational models. This thriving community has been made possible due to the composable nature of Julia's open-source package ecosystem, which fosters collaboration among users while emphasizing rapid prototyping, testing, and reproducibility. Additionally, over the years, Julia has developed a substantial ecosystem of tools and frameworks for algorithmic differentiation (AD), empowering users to create differentiable computational models—both simple and complex—that are specifically tailored to their needs.
We invite contributions that share experiences in developing differentiable computational models—including specific challenges faced, innovative solutions found—and applications of these models across various domains. Attendees can look forward to learning from notable contributors within the Julia community who will present innovative use cases while fostering collaboration among participants. Through networking opportunities and shared insights, we aim to inspire both seasoned users and newcomers by demonstrating how differentiable modelling can enhance model-based CSE workflows!
Graduate research assistant at the University of Texas at Austin
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.