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ANUBIS

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Cerrado

Advanced Numerics for Uncertainty and Bayesian Inference in Science

"Scientific knowledge enters computers through data on the one side, and laws of nature – implicit equations like differential equations and symmetries – on the other. They both provide information, empirical and mechanistic, resp... ver más

31/08/2029

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

EBERHARD KARLS UNIVERSITAET TUEBINGEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-03-22

ERC-2023-COG: ERC CONSOLIDATOR GRANTS Scope:Objectives

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1 Participantes

2.00M€ | Lider

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Duración del proyecto: 65 meses Fecha Inicio: 2024-03-22
Fecha Fin: 2029-08-31

Líder del proyecto

EBERHARD KARLS UNIVERSITAET TUEBINGEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto "Scientific knowledge enters computers through data on the one side, and laws of nature – implicit equations like differential equations and symmetries – on the other. They both provide information, empirical and mechanistic, respectively, crucial to the deduction of new insights. But the algorithms that operate on these sources of information stem from different communities and different eras: machine learning – ""big data"" – on the one hand, and simulation methods – high performance computing – on the other. One of the problems that arise from this disconnect is that inferring latent forces that drive dynamical systems from data requires ""shoehorning"" different algorithms together in inefficient optimization loops; another one is that uncertainty from discretization and emulation is not fully tracked. Probabilistic numerical methods have emerged over the last decade as a holistic view on computation as inference. They provide a unifying language that can leverage empirical and mechanistic information. This proposal outlines a research program to complement and scale probabilistic numerical methods to enrich the quantitative scientist's toolbox along three axes: First, unifying uncertainty from empirical and computational knowledge in one common formalism, which allows the direct and robust combination of simulation and experimentation. Second, developing a rich and practical semantic language for the description of different types of knowledge – mechanistic, empirical, practical. Third, significant computational efficiency gains achieved by managing the computational process globally, instead of as a series of black boxes. Real scientific tasks will provide benchmarks and ensure practical relevance. An open-source software toolbox, complemented by regular summer schools, will ensure that the results reach their audience. As a result, ANUBIS will start a genuinely novel kind of quantitative scientific analysis at the intersection of simulation and machine learning."

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