Data-Enabled Computational Engineering and Applied Quantum Computing Initiative

The world of modern machine learning computation is built on derivatives. In this talk, we will introduce the basics, advantages, and applications of automatic differentiation packages. In this workshop, you will:

• Learn the basics of automatic differentiation;
• Learn the benefits and applications of automatic differentiation;
• Learn how to get started with coding with JAX.

Get the JAX Workshop Materials    

Scientific computing and machine learning have advanced rapidly over the past decade. Many modern applications involve solving complex, high-order multi-physics problems, and processing massive datasets. Parallel computing enables us to divide large computational tasks across multiple processors and platforms, leading to faster solutions, and the ability to tackle larger, more sophisticated models. In this workshop, you will:

• Receive a concise introduction to parallel computing, with a focus on applications relevant to Mechanical and Aerospace Engineering (MAE);
• Engage in hands-on exercises using both CPU- and GPU-based parallel programming;
• Explore how Florida State University researchers apply parallel computing in real-world projects, and learn about available campus resources to help you get started.

Get the MPI & GPU Workshop Materials

In conducting fluid dynamics research, it is often useful to perform preliminary analyses with industrial computational fluid dynamics (CFD) solvers. This hands-on workshop will focus on using the SU2 CFD code. We will go over the fundamentals of mesh generation requirements, running a CFD simulation, and the steps taken for mesh optimization. This workshop will use a combination of Matlab, SU2, and Paraview to accomplish the tasks. In this workshop, you will:

• Learn how to generate meshes analytically;
• Conduct simulations using SU2;
• Learn how to conduct mesh optimization.

Get the CFD Workshop Materials

Modal analysis / modal decomposition techniques play an important role in fluid dynamics research. These techniques can be used to isolate / extract dominant flow mechanisms, as well as to build reduced-order models (ROMs) for applications such as flow estimation & control. This workshop introduces widely used modal decomposition techniques and provides practical, hands-on experience with these tools. In this workshop, you will:

• Get a brief theory/background about popular modal decomposition techniques used in the fluid dynamics community;
• Get hands-on experience in using some of these operator-based and data-driven techniques on toy problems & sample datasets;
• Learn how to make use of these tools in your research.

Get the Modal Analysis Workshop Materials

This workshop will introduce applied machine learning through hands-on numerical experiments. Participants will explore techniques ranging from physics-informed surrogate modeling to advanced topic modeling, along with practical applications of Python for efficient data extraction and wrangling. In this workshop, you will:

• Master data wrangling and extraction using Python;
• Develop surrogate models with and without physics-based constraints;
• Implement topic modeling and text classification;
• Learn about the Engineering Data Analytics Graduate Certificate Program.

Get the Applied Machine Learning Workshop Materials

A literature review is essential in scientific research to understand the field and identify gaps. This hands-on workshop introduces research management and AI-assisted tools for effective literature synthesis. In this workshop, you will learn how to:

• Manage references with Zotero: tagging, collections, and PDFs;
• Organize knowledge in Obsidian: notes, papers, and synthesis;
• Discover trends with ResearchRabbit: literature mapping and network visualization.

Get the Zotero & Obsidian Workshop Materials

A hands-on workshop introducing scientific Python for engineering research, focusing on practical data analysis and visualization using NumPy, pandas, SciPy and Matplotlib. The session concludes with an overview of machine-learning approaches for fluid-dynamics applications and how scientific Python enables modern data-driven modeling and diagnostics.

Get the Scientific Python Workshop Materials

AI-driven surrogate models allow accelerating early-stage design by predicting system performance instantly. Using a vehicle suspension as an example, this workshop shows how to rapidly optimize mechanical, electrical, and thermal systems. In this workshop, you will learn:

• Parametric physical modeling for fast evaluation of system performance;
• Sensitivity analysis to find the most impactful parameters;
• AI-driven surrogate modeling and optimization for rapid trade-offs and design exploration.

Get the AI/ML Workshop Materials

A hands-on introduction to quantum computing concepts and scientific programming. Participants will learn the fundamentals and key steps to do applied quantum computing. In this workshop, you will:

• Explore core concepts: Pauli gates, measurements and randomness;
• Understand quantum phenomena: interference and entanglement (Bell states);
• See quantum advantage with Simon’s Algorithm hands-on example.

Get the Quantum Programming Workshop Materials 

• Set up models for different physics (fluid dynamics, heat transfer and structural mechanics);
• Explore coupling between multiple physical phenomena using COMSOL’s Multiphysics capabilities;
• Visualize simulation results and generate a high-quality plot.

• Type equations, tables and figures with LaTeX;
• Create high-quality plots for publications and presentations;
• Automate data visualization workflows and integrate plots into reports.

• Distribute computations across multiple cores, GPUs, or clusters;
• Speed up large simulations and data analysis tasks;
• Integrate parallel MATLAB with HPC systems for scalable workflows.

• Quantify uncertainty in model parameters;
• Use stochastic sampling for parameter estimation;
• Assess error propagation in simulations.

• Create an RC/HPC account;
• Submit jobs through Slurm scheduler;
• Organize and manage files on the cluster.