Internship: Adaptive Sampling for Training Deep

Le descriptif de l’offre ci-dessous est en Anglais_ **Type de contrat **:Convention de stage **Niveau de diplôme exigé **:Bac + 4 ou équivalent **Fonction **:Stagiaire de la recherche **A propos du centre ou de la direction fonctionnelle**: The Centre Inria de l’Université de Grenoble groups together almost 600 people in 23 research teams and 9 research support departments. Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE,), but also with key economic players in the area. The Centre Inria de l’Université Grenoble Alpe is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world. **Contexte et atouts du poste**: The internship will take place at the DataMove team located in the IMAG building on the campus of Saint Martin d’Heres (Univ. Grenoble Alpes) near Grenoble. The length of the internship is _4 months minimum_ and the start date is flexible, but need a 2 month delay before starting the interhsip due to administrative constraints. The DataMove team is a friendly and stimulating environment that gathers Professors, Researchers, PhD and Master students all leading research on High-Performance Computing. The city of Grenoble is a student-friendly city surrounded by the Alps mountains, offering a high quality of life and where you can experience all kinds of mountain-related outdoor activities. **Mission confiée**: **Subject context** n supervised learning, successfully training advanced neural networks requires annotated data of sufficient quantity and quality. In natural sciences (physics, chemistry, weather modeling), observational data remains to be a limiting factor. One alternative is to numerically create synthetic training data. This offers several advantages: synthetic data can be generated at will, in potentially unlimited amounts, the quality can be degraded in a controlled manner for more robust trainings, and the coverage of the parameter space can be adapted to focus training where relevant. Today, a large variety of simulation codes to create such data are available, from computer graphics, computer engineering, computational physics, biology and chemistry, and so on. When training data is produced from simulation codes, it can be generated along with the training. This approach has multiple benefits. First, there is no need to store and move a huge pre-created data set: float matrices of data can take terrabytes of memory, and reading them from the disk every training iteration might take more time than the iteration itself. Instead, data is stored in working memory and created "on-the-fly": when new data point is created it substitutes an old one. This allows the model to see terrabytes of data throughout its lifetime while storing only a smaller part of it at a time. Second, the training is not done with the same repeated data as in epoch-based approach. Continiously updated training set potentially improves the generalization quality of the model. More importantly, _the update of the training set and creation of new data can be adaptive_, driven by the observed behavior of the neural network during training. However, this adaptive data generation is a challenging question. Active learning adresses this challenge by adaptively sampling the input parameters of simulators based on training progress, aiming to generate more relevant data. Thus, faster and higher-quality training is expected. In current approaches, active learning for simulations-based training often follows a phased algorithm: 1) generate an initial training set by uniformly sampling input points 2) (re)train the model on the trainng set 3) use feedback from the model’s performance to generate/augment new training set and return to (2). Fundamentally, the methods differentiate by choice of "feedback" metric (aquisition function) and the way the next training set is created (aquisition algorithm). **Our research**: Our team's research is focused on exploring and developping new online active learning methods for efficient training of surrogates - neural networks that meant to substitute simulation codes. We have developped _Breed_ for online adaptive surrogate training, such as Physics Informed Neural Networks (PINNs), Neural Operators, and basic Dense Neural Networks, within our _MelissaDL_ framework that allows the training to be highly distributed and the training data to be created on-the-fly. **Our related publications**: **Principales activités**: This intership is focused on investigating use of generative methods for active learning, e.g., diffusion posterior sampling to generate input points based on models un