Phd Student Position in “physical Modelling Via
hace 2 semanas
Physical modelling via generative low-complexity deep neural networks with position-dependent input.
A PhD student is being sought to carry out a project in Deep Neural Networks (DNNs), based in the Dept. of Polymers and Advanced Materials: Physics, Chemistry and Technology, Faculty of Chemistry, University of the Basque Country, San Sebastian, Spain (UPV/EHU) in active collaboration with Dept. of Mechanical Engineering, Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, China (SEU).
The PhD project is a natural continuation of a recent collaboration between Dr. M. A. Gosalvez (UPV/EHU) and Prof. Y. Xing (SEU). Gosalvez and Xing have worked jointly for approximately 17 years, as reflected by at least 20 co-authored publications in peer reviewed journals and 15 contributions in international conferences. Xing’s group is devoted to both experimental and computational work while Gosalvez’s group is dedicated to theoretical and computational modelling. While based in Gosalvez’s group, the PhD student will have the chance to visit Xing’s group twice during 2023-2024.
The project is based on a recent finding by the two groups. For physical systems exhibiting a strong spatial dependence (usually as a function of a few relevant experimental parameters) and when the number of images that display the spatial dependence is very reduced (due to excessive cost/time-consumption/legal-constraints/etc), one can directly use the spatial dependence to segment the images into physically meaningful regions, whereafter every pixel from every image will belong to one region (or class/label), thus resulting in a truly large pixel-based dataset with millions of classified pixels, even when obtained from a few images. The key idea is to use this simple piece of information (the region/class/label) as the output for two equally simple inputs: the experimental conditions and the pixel’s location within the image. In this manner, a low-complexity DNN (with a small number of hidden layers and neurons) can be easily trained into understanding the spatial dependence of the system as a function of the experimental parameters.
Most importantly, after training, the simple DNN can be used out-of-the-box to generate new segmented images in a pixel-by-pixel manner for unknown experimental conditions (generalisation, i.e. interpolation and extrapolation). This generative functionality is remarkable considering the simplicity of the approach (an uncomplicated DNN with spatial-dependent input). Previously, such functionality has been restricted to highly specialised Convolutional Neural Networks (CNNs) with millions of trainable parameters, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). The low complexity of the DNN enables simple/fast modifications/adaptations (e.g. for other projects or in other fields) as well as the use of less-sophisticated computational resources during both training and execution, leading to moderation in power consumption (an increasingly important aspect) while simultaneously simplifying model deployment on mobile devices with limited resources (wherever this aspect may become relevant).
Experience/interest in Physics/Mechanical engineering/Numerical modelling will be highly appreciated. We are trying to build physically meaningful models, one for each of various physical systems, so that we can predict their behaviour under unseen experimental conditions. Previous experience in image segmentation, either by traditional methods (level set, etc ) or by current DNNs, will be highly appreciated. Experience/interest in extrapolation techniques will be highly appreciated as well. The ability of neural networks in general to generalise outside the range where they were trained is currently very weak. The use of image segmentation provides us with a privileged position, since it allows performing a numerical analysis of the dependence of the size and/or shape of the regions (number of pixels, area, centre of mass, typical width, moment of inertia, etc...) with respect to the experimental variables. This can be used to achieve an improved level of extrapolation outside the training range, which CNNs cannot provide (at least yet). Essentially, the difference here is that between data-driven learning (provided by CNNs) and data-driven discovery of underlying physical behaviour (our goal).
**Deadline**: April 21, 2023
**Job category**: PhD Student
**Area**: Maths, Experimental Sciences and Engineering
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