1 citations · 1 across the 5 of their papers we have counts for
7 papers
A Physics-based and Geometry-aware Reduced-order Framework for Accelerated Part-Scale Thermomechanical Simulation of Additive Friction Stir Deposition Process
Apurba Sarker, Sourav Saha
Additive friction stir deposition (AFSD) is attractive for naval and aerospace sustainment because it enables high rate, solid state near net shape fabrication and repair of large…
Separable neural architectures as a primitive for unified predictive and generative intelligence
Reza T. Batley, Apurba Sarker, Rajib Mostakim +2
Intelligent systems across physics, language and perception often exhibit factorisable structure, yet are typically modelled by monolithic neural architectures that do not explicit…
A Kernel-based Resource-efficient Neural Surrogate for Multi-fidelity Prediction of Aerodynamic Field
Apurba Sarker, Reza T. Batley, Darshan Sarojini +1
Surrogate models provide fast alternatives to costly aerodynamic simulations and are extremely useful in design and optimization applications. This study proposes the use of a rece…
Process Microstructure Coupling in Reduced Gravity Laser Welding via Open-Source Multiphysics Simulation Framework
Rakibul Islam Kanak, Taslima Hossain Sanjana, Apurba Sarker +1
Supplying spare parts from Earth for in space repair is economically prohibitive and logistically slow, posing a major barrier to sustainable space operations. As lunar and Martian…
Gravity and Composition Modulated Solidification and Mechanical Properties of Al-Cu Nanostructures
Apurba Sarker, Sourav Saha
The future of space exploration and human settlement beyond Earth hinges on a deeper understanding of in space manufacturing processes. The unique physical conditions and scarcity…
Efficient Aircraft Design Optimization Using Multi-Fidelity Models and Multi-fidelity Physics Informed Neural Networks
Apurba Sarker
Aircraft design optimization traditionally relies on computationally expensive simulation techniques such as Finite Element Method (FEM) and Finite Volume Method (FVM), which, whil…
Graph neural network framework for energy mapping of hybrid monte-carlo molecular dynamics simulations of Medium Entropy Alloys
Mashaekh Tausif Ehsan, Saifuddin Zafar, Apurba Sarker +2
Machine learning (ML) methods have drawn significant interest in material design and discovery. Graph neural networks (GNNs), in particular, have demonstrated strong potential for…