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Explore eigenvalue problems and error control in quantum mechanics, focusing on advanced mathematical and statistical techniques for solving complex computational challenges.
Explore classical and quantum Monte Carlo methods for many-body systems, including Metropolis algorithms, Feynman's path integral formalism, and projector Monte Carlo techniques for fermion systems.
Explore mathematical models of electronic structure, focusing on quantum mechanics applications in materials science and chemistry, presented by Duke University expert Jianfeng Lu.
Explore the paradox of diversity in collective intelligence, examining how cultural trait diversity impacts innovation, coordination, and societal challenges in human societies.
Explore synthetic proto-organisms and challenge dichotomous thinking in this presentation on xenobots, offering insights into collective intelligence and biological engineering.
Discover efficient reinforcement learning techniques for robotic control, balancing self-supervision and human guidance to overcome exploration challenges in high-dimensional spaces.
Explore mathematical theorems on agreement and learning in networks, presented by MIT's Elchanan Mossel at IPAM's Mathematics of Collective Intelligence Workshop.
Explore cognitive and computational foundations of collective human intelligence with MIT's Josh Tenenbaum in this insightful presentation from IPAM's workshop.
Exploring collective problem-solving in insect societies and robotic systems, linking physics and behavior across scales to understand embodied physical intelligence in nature and technology.
Explore variational proofs of isoperimetric inequalities, their generalizations, and open problems in this advanced mathematics lecture by Steven Heilman at IPAM's workshop.
Explore Gibbs sampling for high-dimensional distributions, focusing on convergence guarantees and conductance bounds for sampling from convex bodies. Gain insights into this widely-used algorithm's efficiency and limitations.
Explore explicit and sharp eigenvalue estimates for Neumann p-Laplace eigenvalues in domains represented in Fermi coordinates, presented by Barbara Brandolini at IPAM's Calculus of Variations workshop.
Exploring phase retrieval challenges, conventional methods, and deep learning approaches for scientific imaging. Presents novel solutions for practical phase retrieval problems in unprecedented regimes.
Explore advanced imaging techniques for lithium-ion batteries, revealing nanoscale processes and strain distribution in electrode particles during charge/discharge cycles to improve energy storage technology.
Explore machine learning techniques for enhancing 3D optical diffraction tomography, including digital phantom-based training and physics-constrained neural networks for improved image reconstruction.
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