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Explore robust imaging models using Bayesian inference and deep learning without paired data. Learn techniques for image denoising, segmentation, and unpaired degradation modeling in practical applications.
Explore reduced-order modeling techniques for efficient geophysical exploration, focusing on electromagnetic and seismic methods to solve large-scale forward and inverse problems in real-time.
Explore multivariate portfolio optimization using quantiles, extending univariate approaches to solve complex financial problems and develop new numerical solutions for multivariate preferences.
Explore efficient Bayesian inverse problem solutions using linear surrogate models. Learn about model discrepancy, Gaussian priors, and applications in high-dimensional PDE discretization problems.
Quantifying and allocating risk in electricity grids with renewable energy sources, using Monte Carlo simulations and advanced optimization techniques to enhance grid reliability and cost-effectiveness.
Explore compound flooding modeling, combining storm surge and rainfall simulations. Learn about challenges, recent developments, and applications in coastal environments.
Explore AI-driven climate analysis, focusing on ocean circulation changes. Learn transparent ML techniques rooted in oceanographic theory for trustworthy insights into future climate scenarios.
Multiscale machine learning framework for efficient predictions in enormous output spaces, addressing challenges of limited data and scalability through hierarchical modeling and sparse matrix techniques.
Explores advanced mathematical techniques for electrical impedance tomography, focusing on the monotonicity method's application to extreme, singular, and degenerate inclusions in conductivity reconstruction.
Explore quantitative finance and reinforcement learning in this SIAM talk series, featuring discussions on roll-over risk modeling and continuous-time episodic RL with linear-convex models.
Explore optimal point arrangements in projective spaces, surveying current research and open problems in this fundamental area of metric geometry with applications in digital communication.
Explore mean curvature flow's applications in image processing, data science, and biology. Learn how neural networks can approximate interface evolutions for non-oriented surfaces, with applications to Steiner and Plateau problems.
Explore modeling and simulation of dissolution and precipitation in porous media, focusing on evolving pore-scale geometry and its impact on Darcy scale properties. Learn upscaling techniques and numerical approaches.
Learn effective math modeling report writing techniques, explore a case study on the 2022 practice problem, and discover LaTeX for professional document creation in this comprehensive session.
Explore linear regression, data transformations, and statistical concepts in mathematical modeling. Learn MATLAB examples, sensitivity analysis, and problem-solving techniques for real-world applications.
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