Phd Student : thèse maths appliquées et informatique / machine learning (f/m)
Hybrid
Villeurbanne, France
Internship
24-04-2025
Job Specifications
Requisition #: 16529
Our Mission: Powering Innovation That Drives Human Advancement
When visionary companies need to know how their world-changing ideas will perform, they close the gap between design and reality with Ansys simulation. For more than 50 years, Ansys software has enabled innovators across industries to push boundaries by using the predictive power of simulation. From sustainable transportation to advanced semiconductors, from satellite systems to life-saving medical devices, the next great leaps in human advancement will be powered by Ansys.
Innovate With Ansys, Power Your Career.
Thèse CIFRE ANSYS - LIRIS (contrat CDD de 3 ans)
New data representation of simulation results
Ansys is a global leader in numerical simulation and 3D design software with a proven track record in multiphysics numerical analysis. In association with LIRIS, Ansys offers a CIFRE PhD thesis in the analysis of numerical data carried by meshes.
This initiative covers simulations involving structural mechanics, computational fluid dynamics, electromagnetism, heat transfer, optics, etc., where the complexity and accuracy of the models require high-performance computing resources and generate very large volumes of data. These simulations are carried out using the various Ansys solvers. By leveraging LIRIS' expertise to exploit the geometry of data stored in meshes, Ansys seeks to streamline the storage and utilization of high-precision simulation results, facilitating their remote graphical rendering as they are generated, but also ensuring their long-term archiving.
Some simulation results are particularly difficult to compress due to their complexity and the nature of the data involved:
Structural mechanics simulations analyze stresses and strains resulting from non-regular behaviors such as contact and plasticity. The phenomena involved are geometrically multi-scale: geometric details and changes in material properties require high-resolution discretization carrying solutions with very high local gradients. We therefore use multi-scale simulations in the space domain. When the simulated phenomena evolve over time, there is a multi-scale aspect in time, with global motion that can be of the order of several seconds, while locally, observation frequencies of more than 10000 Hz are necessary.
Simulations in fluid dynamics involve solving highly nonlinear equations, which can result in extremely large and complex transient datasets on models that can have billions of cells.
Simulations in electronics involve complex interactions of electromagnetic fields, often requiring highly refined meshes to capture phenomena with geometric details down to a few nanometers on structures that can be a few tens of centimeters in size with extremely high observation frequencies.
As an example, we plan to consider datasets with hundreds of millions of values and evolving over time with hundreds of time steps.
To this end, Ansys wants to develop compact, lossless or low-loss representations that accuracy is redefined and easily used. To do this, Ansys wishes to get closer to LIRIS laboratory and the results they obtained for identifying and exploiting similarity in geometric data. One of the challenges will be the possibility of compression on the fly, or at least with a controlled memory footprint.
Thesis Subject
The aim of this PhD thesis is to develop new approaches to analyze, represent and store high-precision simulation data obtained on fine meshes (mainly volume meshes, but also surface meshes). To do this, the proposed approach aims at decomposing the result of the simulation on representative bases resulting from a so-called non-local joint analysis. However, the data generated on meshes do not benefit from regular prior structure and the thesis will focus on the obstacles posed by this restriction. This thesis will include numerical aspects to analyze the data independently of its supporting mesh but also algorithmic aspects to take into account the specificity and size of the meshes. It will also be possible to benefit from the optimization power of lightweight neural networks for dimensional reduction of the various problems.
Skills Required
Skills in applied mathematics and computer science (Algorithmic, C++ programming and Python) are required for this thesis, as well as knowledge of numerical optimization tools such as PyTorch and/or PyAnsys tools.
Working Conditions
The doctoral student will divide his or her time between LIRIS (Nautibus building, La Doua campus, Lyon 1 University) and ANSYS (Le Patio building, rue Louis Guérin, Villeurbanne). Both locations are less than a 15minutes walk apart. Villeurbanne is a city right next to Lyon, a dynamic multicultural city of France.
Expected beginning of the thesis: September or October 2025
Bibliography
Mallat, S. G.; Zhang, Z. (1993). “Matching Pursuits with Time-Frequency Dictionaries”. IEEE Transactions on Signal Processing. 1993 (12): 3397–3415.
Aharon, M.; Elad, M.; Bruckstein, A.M. (2006). “The K-SVD: An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation”. IEEE Transactions on Signal Processing. 54 (11): 4311–4322.
Bergeaud, F.; Mallat, S. (1995). “Matching pursuit of images”. Proceedings., International Conference on Image Processing. Vol. 1. pp. 53–56.
J. Digne, R. Chaine, S. Valette, “Self-similarity for accurate compression of point sampled surfaces” Proceedings Eurographics 2014, in Computer Graphics Forum, Wiley, Vol. 33, Number 2, p.155-164,
J. Digne, S. Valette, R. Chaine, “Sparse Geometric Representation Through Local Shape Probing”. IEEE Transactions on Visualization and Computer Graphics 2017
Hamdi-Cherif, J. Digne, R. Chaine, “Super-resolution of Point Set Surfaces using Local Similarities”, Computer Graphics Forum 2017
At Ansys, we know that changing the world takes vision, skill, and each other. We fuel new ideas, build relationships, and help each other realize our greatest potential. We are ONE Ansys. We operate on three key components: our commitments to stakeholders, our values that guide how we work together, and our actions to deliver results. As ONE Ansys, we are powering innovation that drives human advancement
Our Commitments
Amaze with innovative products and solutions
Make our customers incredibly successful
Act with integrity
Ensure employees thrive and shareholders prosper
Our Values
Adaptability: Be open, welcome what’s next
Courage: Be courageous, move forward passionately
Generosity: Be generous, share, listen, serve
Authenticity: Be you, make us stronger
Our Actions
We commit to audacious goals
We work seamlessly as a team
We demonstrate mastery
We deliver outstanding results
VALUES IN ACTION
Ansys is committed to powering the people who power human advancement. We believe in creating and nurturing a workplace that supports and welcomes people of all backgrounds; encouraging them to bring their talents and experience to a workplace where they ...
About the Company
For more than 50 years, Ansys software has enabled innovators across industries to push boundaries with the predictive power of simulation. From sustainable transportation and advanced semiconductors, to satellite systems and life-saving medical devices, the next great leaps in human advancement will be powered by Ansys. Know more
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