Browsing Division of Physical and Mathematical Sciences by Subject "Machine Learning"
Now showing items 1-5 of 5
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Exploring Application of Dendrimer in Energy Storage, Uranyl Extraction and Drug Delivery through Molecular Modelling
This thesis explores the potential applications of poly(amidoamine) (PAMAM) dendrimers, a class of highly branched polymers, using atomistic molecular dynamics simulations. We investigate the use of PAMAM dendrimers in ... -
Learning Filters, Filterbanks, Wavelets and Multiscale Representations
The problem of filter design is ubiquitous. Frequency selective filters are used in speech/audio processing, image analysis, convolutional neural networks for tasks such as denoising, deblurring/deconvolution, enhancement, ... -
Multiscale Modeling of Molecular Sieve Membranes
Gases permeating through nanopores experience selective adsorption with sub-diffusive dynamics. In this work we investigate how the physicochemical properties of nanoporous membranes influence adsorption dynamics of gases, ... -
Prompt and Displaced Signatures of Physics Beyond the Standard Model
The quest to understand our Universe’s fundamental particles and their interactions has led us to the Standard Model (SM) of particle physics. Despite successfully explaining the weak, electromagnetic, and strong ... -
Search for Long-Lived Particles at High Luminosity Large Hadron Collider and Beyond
Despite extensive searches, clear indications of new physics beyond the Standard Model (BSM) remain elusive. Traditionally, experimental searches at the Large Hadron Collider (LHC) during Phase I and phenomenological studies ...