AI Day

Tuesday Jun 16, 2026, 9:00 AM → 4:40 PM Europe/Berlin

Klemperer-Saal, Sächsische Landesbibliothek — Staats- und Universitätsbibliothek Dresden (SLUB), Zellescher Weg 18, 01069 Dresden

9:00 AM → 9:30 AM Registration

9:30 AM → 9:45 AM Welcome and Introduction

9:45 AM → 10:20 AM Coupling HPC Plasma Simulations and AI at Exascale

We present recent results on coupling plasma simulations and large-scale AI models on exascale HPC systems and argue that the workflows we are implementing are the future of I/O.

Speaker: Dr Richard Pausch (Helmholtz-Zentrum Dresden-Rossendorf (HZDR))

10:20 AM → 10:30 AM Q&A

Speaker: Dr Richard Pausch (Helmholtz-Zentrum Dresden-Rossendorf (HZDR))

10:30 AM → 11:05 AM PhysicsNeMo

NVIDIA PhysicsNeMo is an open-source Physics-Informed Machine Learning (Physics-ML) platform. It enables the creation of high-precision, physically based digital twins.

• Predict the time evolution of electric field magnitude inside shielded enclosures
• AI-Driven Prediction of Epoxy Underfill Flow: Capturing Interface Dynamics in a Two-Phase System

Speaker: Dr Abouzar Ghasemi (NVIDIA)

11:05 AM → 11:15 AM Q&A

Speaker: Dr Abouzar Ghasemi (NVIDIA)

11:15 AM → 11:30 AM Break

11:30 AM → 12:05 PM Machine Learning Across Space and Time in Microscopy

Modern microscopes generate large, information-dense images across space
and time, challenging current vision models. I will present our work on
machine learning for quantitative microscopy, including segmentation of
complex biological images, self-supervised learning from videos, and
multiscale vision transformers that integrate fine detail with broader
biological context.

Speaker: Prof. Martin Weigert (TU Dresden)

12:05 PM → 12:15 PM Q&A

Speaker: Prof. Martin Weigert (TU Dresden)

12:15 PM → 1:15 PM Break

1:15 PM → 1:50 PM NVIDIA Cosmos und NVIDIA Omniverse

NVIDIA Cosmos and NVIDIA Omniverse work hand-in-hand to form the cohesive ecosystem for Physical AI,

Omniverse (The Infrastructure and Simulation Environment): Omniverse serves as the base and virtual "data space". Here, physically correct digital twins are created, physical laws are simulated, and environments (e.g., factories, road networks) are mapped using the OpenUSD standard.

Cosmos (The World Foundation Models): Cosmos is the AI model platform that interacts directly with the 3D worlds from Omniverse. Cosmos consists of powerful models such as Cosmos Predict (for simulating world states), Cosmos Transfer (for photorealism), and Cosmos Reason (for physical understanding and decision-making).

Speaker: Dr Pallavi Mohan (NVIDIA)

1:50 PM → 2:00 PM Q&A

Speaker: Dr Pallavi Mohan (NVIDIA)

2:00 PM → 2:15 PM Break

2:15 PM → 2:50 PM Can We Trust AI-Generated Knowledge? From Machine-Scale Outputs to Grounded Scientific Workflows

Scientific knowledge is increasingly produced at machine scale — by large language models, but also as predicted structures, simulations, and data streams. Yet fluent answers are not necessarily correct: LLMs hallucinate and rarely reveal their sources. I will present our work on making AI-generated knowledge trustworthy by grounding LLMs in structured, verifiable knowledge — scholarly knowledge graphs (e.g., SemOpenAlex), multi-agent retrieval-augmented generation with traceable sources (our deployed SQuAI system), and neurosymbolic methods that pair LLM reasoning with knowledge-graph structure.

Speaker: Prof. Michael Färber (TU Dresden)

2:50 PM → 3:00 PM Q&A

3:00 PM → 3:35 PM Shaping Your GenAI: An Overview of Compression and Inference Serving

LLM finetuning adapts a pre-trained foundation model, such as Llama, Mistral, or NVIDIA NeMo models, to domain-, company-, or task-specific needs. After fine-tuning, optimization and compression techniques can improve efficiency, reduce serving costs, and prepare the model for deployment. Choosing the right inference serving approach is then critical to delivering a scalable, production-ready GenAI solution.

NVIDIA provides an end-to-end software and hardware stack for this workflow, including NVIDIA NeMo for LLM development and highly optimized GPU systems for training and inference.

Speaker: Dr Nael Fasfous (NVIDIA)

3:35 PM → 3:45 PM Q&A

Speaker: Dr Nael Fasfous (NVIDIA)

3:45 PM → 4:00 PM Conclusion & Outlook - Wrap up

4:00 PM → 4:40 PM Get Together