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Complexity is easy to recognize but difficult to define there are a host of measures of complexity each relevant for a particular application In Surface engineering self-organization drives the formation of patterns on matter by femtosecond laser irradiation which have important biomedical applications Pattern formation details are not fully understood In work leading to two publications 1 2 via a complexity argument and a physics-guided machine learning framework we show that the severely constrained problem of learning the laser-matter interaction with few data and partial physical knowledge is well-posed in this context Our model allows us to make useful predictions and suggests physical insights In another contribution 3 we propose a new formulation of the Minimum Description Length principle defining model and data complexity in a single step by taking into account signal and noise in training data Experiments indicate that Neural Network classifiers that generalize well follow this principle In unpublished work we propose Taylor entropy a novel measure of dynamical system complexity which can be estimated via a single SEM image This approach could facilitate learning the physical process in new materials through domain adaptation This thesis paves the way for a unified representation of complexity in data and physical knowledge which can be used in the context of Physics-guided machine learning 1 Brandao Eduardo et al Learning PDE to model self-organization of matter Entropy 24 8 2022 1096 2 Brandao Eduardo et al Learning Complexity to Guide Light-Induced Self-Organized Nanopatterns Physical Review Letters 130 22 2023 226201 3 Brandao Eduardo et al Is My Neural Net Driven by the MDL Principle Joint European Conference on Machine Learning and Knowledge Discovery in Databases Cham Springer Nature Switzerland 2023

Publication search with Apprentissage automatique guidé par la physique as keyword

Complexity Methods in Physics-Guided Machine Learning

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