1 citations · 3 across the 6 of their papers we have counts for
7 papers
Geometric and Topological Deep Learning for Predicting Thermo-mechanical Performance in Cold Spray Deposition Process Modeling
Akshansh Mishra
This study presents a geometric deep learning framework for predicting cold spray particle impact responses using finite element simulation data. A parametric dataset was generated…
Atomistic Simulation Guided Convolutional Neural Networks for Thermal Modeling of Friction Stir Welding
Akshansh Mishra
Accurate prediction of temperature evolution is essential for understanding thermomechanical behavior in friction stir welding. In this study, molecular dynamics simulations were p…
Advanced Displacement Magnitude Prediction in Multi-Material Architected Lattice Structure Beams Using Physics Informed Neural Network Architecture
Akshansh Mishra
This paper proposes an innovative method for predicting deformation in architected lattice structures that combines Physics-Informed Neural Networks (PINNs) with finite element ana…
Biomimetic Machine Learning approach for prediction of mechanical properties of Additive Friction Stir Deposited Aluminum alloys based walled structures
Akshansh Mishra
This study presents a novel approach to predicting mechanical properties of Additive Friction Stir Deposited (AFSD) aluminum alloy walled structures using biomimetic machine learni…
Machine Learning-Driven Optimization of TPMS Architected Materials Using Simulated Annealing
Akshansh Mishra
The research paper presents a novel approach to optimizing the tensile stress of Triply Periodic Minimal Surface (TPMS) structures through machine learning and Simulated Annealing…
LatticeML: A data-driven application for predicting the effective Young Modulus of high temperature graph based architected materials
Akshansh Mishra
Architected materials with their unique topology and geometry offer the potential to modify physical and mechanical properties. Machine learning can accelerate the design and optim…
Transport Equation based Physics Informed Neural Network to predict the Yield Strength of Architected Materials
Akshansh Mishra
In this research, the application of the Physics-Informed Neural Network (PINN) model is explored to solve transport equation-based Partial Differential Equations (PDEs). The prima…