28 citations · 60 across the 14 of their papers we have counts for
7 papers · 1 filter
Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls
Harsh Vardhan, David Hyde, Umesh Timalsina +2
Physics simulations like computational fluid dynamics (CFD) are a computational bottleneck in computer-aided design (CAD) optimization processes. To overcome this bottleneck, one r…
Fusion of ML with numerical simulation for optimized propeller design
Harsh Vardhan, Peter Volgyesi, Janos Sztipanovits
In computer-aided engineering design, the goal of a designer is to find an optimal design on a given requirement using the numerical simulator in loop with an optimization method.…
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression
Sarthak Kapoor, Harsh Vardhan, Umesh Timalsina +3
High fidelity design evaluation processes such as Computational Fluid Dynamics and Finite Element Analysis are often replaced with data driven surrogates to reduce computational co…
Deep Active Learning for Regression Using -weighted Hybrid Query Strategy
Harsh Vardhan, Janos Sztipanovits
Designing an inexpensive approximate surrogate model that captures the salient features of an expensive high-fidelity behavior is a prevalent approach in design optimization. In re…
Deep Learning-based Finite Element Analysis (FEA) surrogate for sub-sea pressure vessel
Harsh Vardhan, Janos Sztipanovits
During the design process of an autonomous underwater vehicle (AUV), the pressure vessel has a critical role. The pressure vessel contains dry electronics, power sources, and other…
Reduced Robust Random Cut Forest for Out-Of-Distribution detection in machine learning models
Harsh Vardhan, Janos Sztipanovits
Most machine learning-based regressors extract information from data collected via past observations of limited length to make predictions in the future. Consequently, when input t…
Rare event failure test case generation in Learning-Enabled-Controllers
Harsh Vardhan, Janos Sztipanovits
Machine learning models have prevalent applications in many real-world problems, which increases the importance of correctness in the behaviour of these trained models. Finding a g…