28 citations · 44 across the 12 of their papers we have counts for
12 papers
DynAMO:Dynamic Asset Management Orchestration via Topological Multi-Agent Scheduling
Kanishk Kushwaha, Vikrant Vinod Bansode, Harsh Vardhan +1
While LLM-powered agents offer end-to-end automation for industrial asset lifecycles, real-world Industry 4.0 deployment is hindered by latency, concurrency instability, and safety…
Anvil: An integration of artificial intelligence, sampling techniques, and a combined CAD-CFD tool
Harsh Vardhan, Umesh Timalsina, Michael Sandborn +3
In this work, we introduce an open-source integrated CAD-CFD tool, Anvil, which combines FreeCAD for CAD modeling and OpenFOAM for CFD analysis, along with an AI-based optimization…
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…
Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design
Harsh Vardhan, Peter Volgyesi, Will Hedgecock +1
Automatic underwater vehicle hull Design optimization is a complex engineering process for generating a UUV hull with optimized properties on a given requirement. First, it involve…
Search for universal minimum drag resistance underwater vehicle hull using CFD
Harsh Vardhan, Janos Sztipanovits
In Autonomous Underwater Vehicles (AUVs) design, hull resistance is an important factor in determining the power requirements and range of vehicle and consequently affect battery s…
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…
An Improved Algorithm for Clustered Federated Learning
Harshvardhan, Avishek Ghosh, Arya Mazumdar
In this paper, we address the dichotomy between heterogeneous models and simultaneous training in Federated Learning (FL) via a clustering framework. We define a new clustering mod…
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…
Tackling benign nonconvexity with smoothing and stochastic gradients
Harsh Vardhan, Sebastian U. Stich
Non-convex optimization problems are ubiquitous in machine learning, especially in Deep Learning. While such complex problems can often be successfully optimized in practice by usi…
Stochastic Compositional Gradient Descent under Compositional Constraints
Srujan Teja Thomdapu, Harshvardhan, Ketan Rajawat
This work studies constrained stochastic optimization problems where the objective and constraint functions are convex and expressed as compositions of stochastic functions. The pr…