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drift, environmental stochasticity, and double crossovers). • Study the complex feedback loops between the evolution of genetic load within inversions and the spatial dynamics of multiple inversions
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modelling: -Weighted PINNs, -Bayesian PINNs, -Stochastic PINNs, -Ensemble PINNs, -Domain-decomposition PINNs. Selected approaches will be tested within a dedicated data-assimilation framework
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stochastic modeling, Bayesian inference, data fusion and modern machine learning. Its research activities span various application domains such as security, non-destructive testing, infrared imaging and
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(2019). [7] Y. Yang, P. Perdikaris, Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems, arXiv:1901.04878 (2019). 8] L. Lu, J. Pengzhan, P. Guofei, G