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of neural networks, information-theoretic and optimal-transport perspectives on representation and generalisation, probabilistic numerics and Bayesian deep learning, and emerging frameworks for scientific
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applications. This includes, but is not limited to, stochastic differential equations, stochastic partial differential equations, variational and geometric methods, probabilistic numeric, optimal transport, and
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workflows, and differentiable or probabilistic modelling approaches, to support robust calibration, sensitivity analysis and uncertainty quantification; abstract biological mechanisms into reusable design
Searches related to probabilistic forecasting
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