arXiv subjects

Harsh Vardhan

Publications and source records attributed to Harsh Vardhan.

29 records · Page 2Linked to original sources

arXiv2023

Search for universal minimum drag resistance underwater vehicle hull using CFD

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 size, weight, and volume requirement of the design. In this paper, we leverage on AI-based optimization algorithm along with Computational Fluid Dynamics (CFD) simulation to study the optimal hull design that minimizing the resistance. By running the CFD-based optimization at different operating velocities and turbulence intensity, we want to study/search the possibility of a universal design that will provide least resistance/near-optimal design across all operating conditions (operating velocity) and environmental conditions (turbulence intensity). Early result demonstrated that the optimal design found at low velocity and low turbulence condition performs very poor at high velocity and high turbulence conditions. However, a design that is optimal at high velocity and high turbulence conditions performs near-optimal across many considered velocity and turbulence conditions.

cs.CE↗

arXiv2023

Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design

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 involves the integration of involved computationally complex engineering simulation tools. Second, it needs integration of a sample efficient optimization framework with the integrated toolchain. To this end, we integrated the CAD tool called FreeCAD with CFD tool openFoam for automatic design evaluation. For optimization, we chose Bayesian optimization (BO), which is a well-known technique developed for optimizing time-consuming expensive engineering simulations and has proven to be very sample efficient in a variety of problems, including hyper-parameter tuning and experimental design. During the optimization process, we can handle infeasible design as constraints integrated into the optimization process. By integrating domain-specific toolchain with AI-based optimization, we executed the automatic design optimization of underwater vehicle hull design. For empirical evaluation, we took two different use cases of real-world underwater vehicle design to validate the execution of our tool.

cs.RO↗

arXiv2023

Fusion of ML with numerical simulation for optimized propeller design

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. In this design optimization process, a good design optimization process is one that can reduce the time from inception to design. In this work, we take a class of design problem, that is computationally cheap to evaluate but has high dimensional design space. In such cases, traditional surrogate-based optimization does not offer any benefits. 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. By using this trained surrogate model with the traditional optimization method, we can get the best of both worlds. We call this as Surrogate Assisted Optimization (SAO)- a hybrid approach by mixing ML surrogate with the traditional optimization method. Empirical evaluations of propeller design problems show that a better efficient design can be found in fewer evaluations using SAO.

cs.LG↗

arXiv2022

ASTROMER: A transformer-based embedding for the representation of light curves

Taking inspiration from natural language embeddings, we present ASTROMER, a transformer-based model to create representations of light curves. ASTROMER was pre-trained in a self-supervised manner, requiring no human-labeled data. We used millions of R-band light sequences to adjust the ASTROMER weights. The learned representation can be easily adapted to other surveys by re-training ASTROMER on new sources. The power of ASTROMER consists of using the representation to extract light curve embeddings that can enhance the training of other models, such as classifiers or regressors. As an example, we used ASTROMER embeddings to train two neural-based classifiers that use labeled variable stars from MACHO, OGLE-III, and ATLAS. In all experiments, ASTROMER-based classifiers outperformed a baseline recurrent neural network trained on light curves directly when limited labeled data was available. Furthermore, using ASTROMER embeddings decreases computational resources needed while achieving state-of-the-art results. Finally, we provide a Python library that includes all the functionalities employed in this work. The library, main code, and pre-trained weights are available at https://github.com/astromer-science

astro-ph.IM↗

arXiv2022

Deep Learning-based Finite Element Analysis (FEA) surrogate for sub-sea pressure vessel

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 sensors that can not be flooded. A traditional design approach for a pressure vessel design involves running multiple Finite Element Analysis (FEA) based simulations and optimizing the design to find the best suitable design which meets the requirement. Running these FEAs are computationally very costly for any optimization process and it becomes difficult to run even hundreds of evaluation. In such a case, a better approach is the surrogate design with the goal of replacing FEA-based prediction with some learning-based regressor. 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. The challenge of creating a surrogate for a class of problems is data generation. Since the process is computationally costly, it is not possible to densely sample the design space and the learning response surface on sparse data set becomes difficult. During experimentation, we observed that a Deep Learning-based surrogate outperforms other regression models on such sparse data. In the present work, we are utilizing the Deep Learning-based model to replace the costly finite element analysis-based simulation process. By creating the surrogate we speed up the prediction on the other design much faster than direct Finite element Analysis. We also compared our DL-based surrogate with other classical Machine Learning (ML) based regression models( random forest and Gradient Boost regressor). We observed on the sparser data, the DL-based surrogate performs much better than other regression models.

cs.LG↗

arXiv2022

Deep Active Learning for Regression Using $ε$-weighted Hybrid Query Strategy

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 recent times, Deep Learning (DL) models are being used as a promising surrogate computational model for engineering problems. 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. In the present work, we propose a novel sampling technique by combining the active learning (AL) method with DL. We call this method $ε$-weighted hybrid query strategy ($ε$-HQS) , which focuses on the evaluation of the surrogate at each learning iteration and provides an estimate of the failure probability of the surrogate in the Design Space. By reusing already collected training and test data, the learned failure probability guides the next iteration's sampling process to the region of the high probability of failure. During the empirical evaluation, better accuracy of the surrogate was observed in comparison to other methods of sample selection. We empirically evaluated this method in two different engineering design domains, finite element based static stress analysis of submarine pressure vessel(computationally costly process) and second submarine propeller design( high dimensional problem). https://github.com/vardhah/epsilon_weighted_Hybrid_Query_Strategy

cs.LG↗

arXiv2022

Reduced Robust Random Cut Forest for Out-Of-Distribution detection in machine learning models

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 to these trained models is data with significantly different statistical properties from data used for training, there is no guarantee of accurate prediction. Consequently, using these models on out-of-distribution input data may result in a completely different predicted outcome from the desired one, which is not only erroneous but can also be hazardous in some cases. Successful deployment of these machine learning models in any system requires a detection system, which should be able to distinguish between out-of-distribution and in-distribution data (i.e. similar to training data). 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. Similar to the Robust Random Cut Forest (RRCF), RRRCF is a structured, but a reduced representation of the training data sub-space in form of cut trees. Empirical results of this method on both low and high-dimensional data showed that inference about data being in/out of training distribution can be made efficiently and the model is easy to train with no difficult hyper-parameter tuning. The paper discusses two different use-cases for testing and validating results.

cs.LG↗

arXiv2022

Modeling and Optimization of a Longitudinally-Distributed Global Solar Grid

Our simulation-based experiments are aimed to demonstrate a use case on the feasibility of fulfillment of global energy demand by primarily relying on solar energy through the integration of a longitudinally-distributed grid. These experiments demonstrate the availability of simulation technologies, good approximation models of grid components, and data for simulation. We also experimented with integrating different tools to create realistic simulations as we are currently developing a detailed tool-chain for experimentation. These experiments consist of a network of model houses at different locations in the world, each producing and consuming only solar energy. The model includes houses, various appliances, appliance usage schedules, regional weather information, floor area, HVAC systems, population, number of houses in the region, and other parameters to imitate a real-world scenario. Data gathered from the power system simulation is used to develop optimization models to find the optimal solar panel area required at the different locations to satisfy energy demands in different scenarios.

cs.CY↗

arXiv2022

Web-Based Platform for Evaluation of Resilient and Transactive Smart-Grids

Today's smart-grids have seen a clear rise in new ways of energy generation, transmission, and storage. This has not only introduced a huge degree of variability, but also a continual shift away from traditionally centralized generation and storage to distributed energy resources (DERs). In addition, the distributed sensors, energy generators and storage devices, and networking have led to a huge increase in attack vectors that make the grid vulnerable to a variety of attacks. The interconnection between computational and physical components through a largely open, IP-based communication network enables an attacker to cause physical damage through remote cyber-attacks or attack on software-controlled grid operations via physical- or cyber-attacks. Transactive Energy (TE) is an emerging approach for managing increasing DERs in the smart-grids through economic and control techniques. Transactive Smart-Grids use the TE approach to improve grid reliability and efficiency. However, skepticism remains in their full-scale viability for ensuring grid reliability. In addition, different TE approaches, in specific situations, can lead to very different outcomes in grid operations. In this paper, we present a comprehensive web-based platform for evaluating resilience of smart-grids against a variety of cyber- and physical-attacks and evaluating impact of various TE approaches on grid performance. We also provide several case-studies demonstrating evaluation of TE approaches as well as grid resilience against cyber and physical attacks.

cs.ET↗

arXiv2022

Rare event failure test case generation in Learning-Enabled-Controllers

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 good test case that can reveal the potential failure in these trained systems can help to retrain these models to increase their correctness. For a well-trained model, the occurrence of a failure is rare. Consequently, searching these rare scenarios by evaluating each sample in input search space or randomized search would be costly and sometimes intractable due to large search space, limited computational resources, and available time. In this paper, we tried to address this challenge of finding these failure scenarios faster than traditional randomized search. The central idea of our approach is to separate the input data space in region of high failure probability and region of low/minimal failure probability based on the observation made by training data, data drawn from real-world statistics, and knowledge from a domain expert. Using these information, we can design a generative model from which we can generate scenarios that have a high likelihood to reveal the potential failure. We evaluated this approach on two different experimental scenarios and able to speed up the discovery of such failures a thousand-fold faster than the traditional randomized search.

cs.LG↗

arXiv2022

Tackling benign nonconvexity with smoothing and stochastic gradients

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 using stochastic gradient descent (SGD), theoretical analysis cannot adequately explain this success. In particular, the standard analyses do not show global convergence of SGD on non-convex functions, and instead show convergence to stationary points (which can also be local minima or saddle points). We identify a broad class of nonconvex functions for which we can show that perturbed SGD (gradient descent perturbed by stochastic noise -- covering SGD as a special case) converges to a global minimum (or a neighborhood thereof), in contrast to gradient descent without noise that can get stuck in local minima far from a global solution. For example, on non-convex functions that are relatively close to a convex-like (strongly convex or PL) function we show that SGD can converge linearly to a global optimum.

cs.LG↗