arXiv subjects

Soban Nasir Lone

Publications and source records attributed to Soban Nasir Lone.

3 recordsLinked to original sources

arXiv2026

Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth

Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose challenges for practical deployment in intelligent transportation systems (ITS). While recent work has proposed efficient alternatives to STGNNs, a fundamental question remains unexplored: are these architectures themselves over-parameterised? We examine this question using the Spatio-Temporal Graph Convolutional Network (STGCN), one of the most widely adopted models in this domain. Through systematic experiments across four diverse traffic datasets, we compare 1-block, 2-block (standard), and 3-block STGCN variants. Our findings reveal that the single-block architecture achieves optimal performance for short-term prediction (10 mins) on three of four datasets, while incurring only marginal degradation ($\leq$1.8% relative error) at longer horizons. Crucially, the 2-block variant incurs 61% higher CPU inference latency and 37% lower throughput relative to 1-block -- substantial overhead for resource-constrained ITS deployment. The 3-block architecture offers no favourable tradeoff, more than doubling computational cost for $<$0.5% relative improvement. These results suggest that the default 2-block STGCN may be over-parameterised for many applications, with implications for both practitioners deploying traffic prediction systems and researchers benchmarking efficiency-focused methods.

cs.LG↗

arXiv2025

Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification

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. By incorporating Bayesian neural networks as the building blocks for the branch and trunk networks, our framework endows DeepONet with uncertainty quantification. The use of Rényi's $α$-divergence, instead of the Kullback-Leibler divergence (KLD), commonly used in standard variational inference, mitigates issues related to prior misspecification that are prevalent in Variational Bayesian DeepONets. This approach offers enhanced flexibility and robustness. We demonstrate that modifying the variational objective function yields superior results in terms of minimising the mean squared error and improving the negative log-likelihood on the test set. Our framework's efficacy is validated across various mechanical systems, where it outperforms both deterministic and standard KLD-based VI DeepONets in predictive accuracy and uncertainty quantification. The hyperparameter $α$, which controls the degree of robustness, can be tuned to optimise performance for specific problems. We apply this approach to a range of mechanics problems, including gravity pendulum, advection-diffusion, and diffusion-reaction systems. Our findings underscore the potential of $α$-VI DeepONet to advance the field of data-driven operator learning and its applications in engineering and scientific domains.

stat.ML↗

arXiv2025

A recursive Bayesian neural network for constitutive modeling of sands under monotonic and cyclic loading

In geotechnical engineering, constitutive models are central to capturing soil behavior across diverse drainage conditions, stress paths,and loading histories. While data driven deep learning (DL) approaches have shown promise as alternatives to traditional constitutive formulations, their deployment requires models that are both accurate and capable of quantifying predictive uncertainty. This study introduces a recursive Bayesian neural network (rBNN) framework that unifies temporal sequence learning with generalized Bayesian inference to achieve both predictive accuracy and rigorous uncertainty quantification. A key innovation is the incorporation of a sliding window recursive structure that enables the model to effectively capture path dependent soil responses under monotonic and cyclic loading. By treating network parameters as random variables and inferring their posterior distributions via generalized variational inference, the rBNN produces well calibrated confidence intervals alongside point predictions.The framework is validated against four datasets spanning both simulated and experimental triaxial tests: monotonic loading using a Hardening Soil model simulation and 28 CD tests on Baskarp sand, and cyclic loading using an exponential constitutive simulation of CD CU tests and 37 experimental cyclic CU tests on Ottawa F65 sand. This progression from monotonic to cyclic and from simulated to experimental data demonstrates the adaptability of the proposed approach across varying levels of data fidelity and complexity. Comparative analyses with LSTM, Encoder Decoder,and GRU architectures highlight that rBNN not only achieves competitive predictive accuracy but also provides reliable confidence intervals.

cs.LG↗