stage - Maison de la Simulation

Enhancing heat equation Simulations with AI-Driven In-Situ Analysis Using High-Performance Computing

Superviseur : Martial MANCIP, Benoît MARTIN, Yushan WANG
Durée du stage : 6 months (from february 2024)
Langue : french or english
Lieu : Maison de la Simulation, CEA Saclay

Context

This master internship focuses on leveraging Artificial Intelligence (AI) for High-Performance Computing (HPC) simulations in the field of heat equation.
The project aims to integrate AI-based techniques within a heat simulation code to enable in-situ analysis and inference, optimizing the post-treatment process and enhancing its outputs capabilities.

Objective

The primary goal of this internship is to develop and implement an AI-driven methodology within a heat equation simulation framework for real-time heat source detection (so-call event) and labeling in small simulation boxes. These labeled events, represented through 3D renderings or ensembles of 2D slices, will serve as training, validation, and test datasets for the AI model. Subsequently, the trained AI model will be integrated into the DEISA framework available with the simulator to conduct in-situ simulations with enhanced inference capabilities.

Science of Computing team

DEISA: dask-enabled in situ analytics
https://cea.hal.science/hal-03509198v1

Methodology

Data Generation and Labeling: Use the HPC simulator to create small simulation boxes. Implement algorithms to detect and label one or two specific events within these boxes.
Dataset Preparation: Construct training, validation, and test sets using 3D renderings or 2D slices generated from the labeled events.
AI Model Development: Design and train an AI model, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), to recognize and classify events within the simulation data.
Integration with simulator and DEISA:
Implement the AI model within the DEISA framework for in-situ simulations anylisis.
Use the AI model to perform real-time inference during simulation runs.

Expected Outcomes

A labeled dataset of events within small simulation boxes.
Trained AI model capable of accurately detecting and labeling events in simulation data.
Integration of the AI model within the DEISA framework for in-situ simulations with enhanced inference capabilities to be abble to feedback high frequency outputs in the simulation.
Evaluation of the AI’s performance in improving simulation accuracy, efficiency, and predictive capabilities.

We expect to add the use of a feedback process from DEISA to the simulation to switch on high frequency outputs when the AI detects one event and stops them with it has vanished.

Conclusion

The successful implementation of AI techniques within the simulator code for in-situ analysis has the potential to significantly enhance the efficiency of the output production of simulations.

This internship provides a stepping stone towards the integration of cutting-edge AI methodologies with HPC simulations, opening doors for more precise predictions and deeper insights into complex phenomena.

Candidate profil

Parallel computing
C++
Python3
AI : DL with Tensorflow or Pytorch

How to candidate

Send cover letter and CV to contact@mdls.fr

Context

The aim of the project is to build virtual display walls with multiple resolutions and interactions using Augmented Reality (AR). This project will be based on the high-end multiple resolution data visualisation system at Maison de la Simulation (https://mdls.fr/).

Specifically, we have been developing tools for the data analysis of numerical simulations either in-situ while running on a super-computer or with data written on disks. We work on the visualisation of ensemble of simulations through the TiledViz infrastructure to build efficient analysis and visualisations of the results of massively-parallel simulations. We also use artificial intelligence and machine learning approaches to analyse complex data as produced in medicine/biology applications.

This internship is funded by CEA and will be conducted mostly at Maison de la Simulation and partly at LISN (Université Paris-Saclay). We aim to develop this topic to a Ph.D. project on the interaction of multiple users during distance collaborative sessions.

It would start at best in February 2024 for a period of six months after a security clearance, and its remuneration will depend on the CEA grid according to the candidate’s training and experience.

Internship objectives

The focus of this project is to allow remote users to access this high-end infrastructure TiledViz using optical-see-through AR head-mounted display (HMD) to visualise and explore complex datasets. A typical scenario would be for this system to be used in collaborative meetings of people of various expertise to analyse scientific data.

There are several aspects to be considered in this project:
1) Transform 3D interactions with hand gestures, voice commands, etc. captured from the HMD device to 2D interactions that would be processed by TiledViz;

2) Capture the data flow from TiledViz infrastructure (located at Maison de la Simulation) to readapt it as a virtual wall to the
current context of interaction of the remote users;

3) Adapt the rendering resolution on the HMD based on the distance of the user to the virtual wall;

4) Evaluate the performance and user experience of using of virtual wall via AR headsets and the real TiledViz system.