no code implementations • 24 Apr 2023 • Harsh Vardhan, David Hyde, Umesh Timalsina, Peter Volgyesi, Janos Sztipanovits
In this work, we leverage recent advances in optimization and artificial intelligence (AI) to explore both of these potential approaches, in the context of designing an optimal unmanned underwater vehicle (UUV) hull.
no code implementations • 4 Mar 2023 • Sumit Kumar, Harsh Vardhan, Sneha Priya, Ayush Kumar
The latest WHO report showed that the number of malaria cases climbed to 219 million last year, two million higher than last year.
no code implementations • 28 Feb 2023 • Harsh Vardhan, Peter Volgyesi, Janos Sztipanovits
In this work, we propose an alternative way to use ML model to surrogate the design process that formulates the search problem as an inverse problem and can save time by finding the optimal design or at least a good initial seed design for optimization.
1 code implementation • 28 Feb 2023 • Harsh Vardhan, Peter Volgyesi, Will Hedgecock, Janos Sztipanovits
Second, it needs integration of a sample efficient optimization framework with the integrated toolchain.
no code implementations • 18 Feb 2023 • Harsh Vardhan, Janos Sztipanovits
However, a design that is optimal at high velocity and high turbulence conditions performs near-optimal across many considered velocity and turbulence conditions.
1 code implementation • 17 Jan 2023 • Xiaofan Yu, Ludmila Cherkasova, Harsh Vardhan, Quanling Zhao, Emily Ekaireb, Xiyuan Zhang, Arya Mazumdar, Tajana Rosing
To fully unleash the potential of Async-HFL in converging speed under system heterogeneities and stragglers, we design device selection at the gateway level and device-gateway association at the cloud level.
1 code implementation • 16 Nov 2022 • Harsh Vardhan, Umesh Timalsina, Peter Volgyesi, Janos Sztipanovits
In a computer-aided engineering design optimization problem that involves notoriously complex and time-consuming simulator, the prevalent approach is to replace these simulations with a data-driven surrogate that approximates the simulator's behavior at a much cheaper cost.
1 code implementation • 24 Jun 2022 • Harsh Vardhan, Janos Sztipanovits
However, the main challenge in creating a DL-based surrogate is to simulate/label a large number of design points, which is time-consuming for computationally costly and/or high-dimensional engineering problems.
no code implementations • 18 Jun 2022 • Harsh Vardhan, Janos Sztipanovits
In this paper, we introduce a novel approach for this detection process using a Reduced Robust Random Cut Forest (RRRCF) data structure, which can be used on both small and large data sets.
1 code implementation • 11 Jun 2022 • Harsh Vardhan, Janos Sztipanovits
Finding a good test case that can reveal the potential failure in these trained systems can help to retrain these models to increase their correctness.
no code implementations • 6 Jun 2022 • Harsh Vardhan, Janos Sztipanovits
Once the surrogate is trained for a class of problem, then the learned response surface can be used to analyze the stress effect without running the FEA for that class of problem.
no code implementations • 2 May 2022 • C. Donoso-Oliva, I. Becker, P. Protopapas, G. Cabrera-Vives, Vishnu M., Harsh Vardhan
We used millions of R-band light sequences to adjust the ASTROMER weights.
no code implementations • 18 Feb 2022 • Harsh Vardhan, Sebastian U. Stich
Non-convex optimization problems are ubiquitous in machine learning, especially in Deep Learning.