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Greening the Economy: Sustainable Cities
Introduction to Graphic Illustration
Computational Social Science Methods
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Explorez le contrôle stochastique pour optimiser les traitements médicaux. Découvrez les applications mathématiques avancées dans la prise de décision clinique.
Explorez la théorie de l'ordre convexe fonctionnel pour les processus stochastiques, avec une approche constructive et simulable présentée par Gilles Pagès.
Explore optimal revealed utilities and convex pricing kernels from a forward perspective in stochastic analysis and numerical probability.
Explore thermodynamic limits in neuroscience through two examples, gaining insights into stochastic analysis and numerical probability in mathematical biology.
Explore conditional propagation of chaos in generalized Hawkes processes with Eva Löcherbach's mathematical analysis of stochastic systems and numerical probability.
Explore billiards, outer billiards, and symplectic billiards with Richard Evan Schwartz. Discover mathematical concepts and programs like Mc billiard in this engaging conference talk.
Explore finite neuron method fundamentals, covering finite element method, neural networks, and adaptive PD. Gain insights into mathematical concepts and their applications.
Explore linear and nonlinear schemes for forward model reduction and inverse problems in this advanced mathematics lecture by Olga Mula.
Explore learning operators for scientific machine learning, covering numerical experiments, generalization, and applications in inverse problems and imaging.
Explore finite neuron method, covering electrical entropy, curse of dimensionality, neural networks, and approximation properties. Gain insights into deep learning concepts and techniques.
Explore advanced concepts in operator learning, focusing on mathematical techniques and applications in scientific machine learning.
Explore advanced mathematical concepts in finite neuron methods through an in-depth lecture by Jinchao Xu, focusing on theoretical foundations and practical applications.
Explore learning operators in scientific machine learning, covering traditional methods, neural networks, and practical applications for mathematical problem-solving.
Explore data-driven latent representations for time-dependent problems, focusing on applications in climate downscaling, superresolution, and optimal transport.
Explore data-driven high fidelity CFD, covering shock droplet interaction, icing, multiscale analysis, DG schemes, and scientific machine learning applications in computational fluid dynamics.
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