Speaker
Marcell Stippinger
(HUN-REN Wigner FK RMI Komputációs Tudományok Osztálya)
Description
In this talk, we present three use cases demonstrating how AI tools are advancing research at the HUN-REN Wigner Research Centre for Physics as part of the AI 4 Science program.
- Literature Review with Large Language Models (LLMs): We leverage large language models to streamline literature searches and process complex scientific documents. Recent state-of-the-art open-source models are integrated into the HUN-REN GenAI 4 Science platform, and lighter-weight models can also be run efficiently on local hardware.
- Generative AI for Scientific Writing and Coding: We utilize generative AI to enhance scientific writing and assist code generation via tools like GitHub Copilot. This significantly accelerates the development of test cases, automatic documentation, and visualization workflows.
- Exploring Physics-Informed Neural Networks (PINNs): To deepen our understanding of physics-informed neural networks, we've launched a journal club focused on their theoretical and practical aspects. PINNs combine data-driven learning with the rigor of physical laws: they fit observed data while ensuring compliance with (possibly incomplete) differential equations and boundary conditions through automatic differentiation. This emerging field raises practical challenges, such as tuning network depth, width, activation functions, and loss balance; moreover, fundamental questions about whether a single network can represent the solution space for all initial conditions. While our exploration is still in the early stages, there is growing interest in applications ranging from tokamak plasma diagnostics to reconstructing electric potentials in spiking biological neural networks.
Primary author
Marcell Stippinger
(HUN-REN Wigner FK RMI Komputációs Tudományok Osztálya)
