Phd Student : thèse maths appliquées et informatique / machine learning (f/m)

 Requisition #: 16529 

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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. 

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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

    A. 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:

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Our Actions:

• We commit to audacious goals
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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 are valued and can thrive.  

Our culture is grounded in our four core values of adaptability, courage, generosity, and authenticity. Through our behaviors and actions, these values foster higher team performance and greater innovation for our customers.  

We’re proud to offer programs, available to all employees, to further impact innovation and business outcomes, such as employee networks and learning communities that inform solutions for our globally minded customer base. 

WELCOME WHAT’S NEXT IN YOUR CAREER AT ANSYS

At Ansys, you will find yourself among the sharpest minds and most visionary leaders across the globe. Collectively, we strive to change the world with innovative technology and transformational solutions. With a prestigious reputation in working with well-known, world-class companies, standards at Ansys are high — met by those willing to rise to the occasion and meet those challenges head on. Our team is passionate about pushing the limits of world-class simulation technology, empowering our customers to turn their design concepts into successful, innovative products faster and at a lower cost. Ready to feel inspired? Check out some of our recent customer stories, here and here.

At Ansys, it’s about the learning, the discovery, and the collaboration. It’s about the “what’s next” as much as the “mission accomplished.” And it’s about the melding of disciplined intellect with strategic direction and results that have, can, and do impact real people in real ways. All this is forged within a working environment built on respect, autonomy, and ethics.
 

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Ansys is an S&P 500 company and a member of the NASDAQ-100. We are proud to have been recognized for the following more recent awards, although our list goes on: Newsweek’s Most Loved Workplace globally and in the U.S., Gold Stevie Award Winner, America’s Most Responsible Companies, Fast Company World Changing Ideas, Great Place to Work Certified (China, Greece, France, India, Japan, Korea, Spain, Sweden, Taiwan, and U.K.).

 
For more information, please visit us at www.ansys.com
 

Ansys is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other protected characteristics.
 
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