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Field
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identifying real households or companies. But energy data is not like images or text: it consists of time series living on a physical network, governed by power-flow equations. Off-the-shelf generative models
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the following areas: partial differential equations, inverse problems, numerical methods for PDEs, scientific computing; the ability and interest to work with both rigorous mathematical arguments and
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new approach has emerged that integrates data and mathematical models through neural networks. This has led to the development of a method for solving partial differential equations (PDEs) known as
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differential equations that require careful modeling and analysis. Imaging optics involves the design and optimization of imaging systems, such as cameras and telescopes, to most accurately capture and reproduce
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transfer across different robotic platforms and morphologies. More generally, the dynamics of a robot can be represented by a generic differential equation describing the evolution of its state (x) as a
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differential equations (PDEs). Full-waveform inversion is a powerful approach to such problems, but its practical use is limited by the strong nonlinearity of the inverse problem and by the computational cost
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will consider techniques like flow matching, and use ideas from optimal transport and neural (stochastic) differential equations, invariant Kalman filtering and geometric numerical integration
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will start in Leiden, transfer after a year to Lund for two years, with a final year in Leiden. You will consider nonlinear wave equations with spatial inhomogeneities and use tools from dynamical
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households or companies. But energy data is not like images or text: it consists of time series living on a physical network, governed by power-flow equations. Off-the-shelf generative models produce data
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transport and neural (stochastic) differential equations, invariant Kalman filtering and geometric numerical integration. The application areas will be chosen among the use cases of the aiD canter