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natural processes and technological applications, from atmospheric phenomena like rain clouds to industrial processes such as spray cooling of electronics. Due to their ubiquitous presence, droplets have
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) have shown promise in incorporating physical constraints into the learning process. Recent developments in geometric deep learning, graph NN and neural operators have further expanded the potential
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this paradigm to general history-dependent / rate-dependent behaviours still remains challenging and will be addressed in this project. The paradigm of model-free data-driven computational mechanics (DDCM
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