Bayesian Inference
Explore my Bayesian inference projects, where I utilise probabilistic modelling and statistical methods to address various problems.
Posts by Collection
portfolio
Data Science projects
Check out my data science related projects, including exploratory data analysis, machine learning and deep learning model applications, generative AI, and Bayesian statistics.
Machine Learning From Scratch
Explore the mathematical aspects of ML models by building them from scratch based on their formulations.
Math-Related Projects
Dive into my Math-related projects utilizing concepts from Linear Algebra, Image Processing, Partial Differential Equations, and Fluid Mechanics.
Operator Learning
Discover my projects on Operator Learning, applying Deep Learning models to learn Differential Equations.
Physics-Informed Neural Networks
Explore my Physics-Informed Neural Networks projects applied to practical problems in Mechanics.
publications
Alpha-VI DeepONet
Published in arXiv preprint, 2024
We introduce a novel deep operator network (DeepONet) framework that incorporates generalised variational inference (GVI) using Rényi’s α-divergence to learn complex operators while quantifying uncertainty. We apply this approach to a range of mechanics problems, including gravity pendulum, advection-diffusion, and diffusion-reaction systems.
Recommended citation: S. N. Lone, S. De, R. Nayek, Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification, arXiv preprint arXiv:2408.00681 (2024)
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Recursive Bayesian neural networks
Published in arXiv preprint, 2024
This study proposes a recursive Bayesian neural network (rBNN) framework for uncertainty-aware constitutive modelling in geotechnical engineering, incorporating a sliding window approach to capture temporal dependencies. Validated on numerical and experimental triaxial datasets, the rBNN provides robust confidence intervals, highlighting trade-offs between deterministic and probabilistic models.
Recommended citation: Noor, T., Nasir Lone, S., Ramana, G.V. and Nayek, R., 2025. A recursive Bayesian neural network for constitutive modeling of sands under monotonic loading. arXiv e-prints, pp.arXiv-2501.
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talks
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching assistant for other courses
Courses, Indian Institute of Technology, Delhi, 2022
The courses for which I was the teaching assistant throughout my time at the Indian Institute of Technology, Delhi are as given.
Teaching assistant for the course Machine Learning for Mechanics
Undergraduate course (APL405), Indian Institute of Technology, Delhi, 2024
Machine learning for mechanics (APL405) is an introductory course to statistical machine learning for students with some background in calculus, linear algebra and statistics. The course is focusing on supervised learning, i.e, classification and regression. My role in this course was to assist with the hands-on coding sessions and grading of assignments.