Work

Comparative Analysis of Inverse Problem-Solving Techniques: Integrating Deep Learning with Advanced Optimization Methods

Year: 2024

Type: article

Abstract: This paper presents a comparative analysis of four distinct approaches to solving inverse problems in engineering and scientific applications. The studies under comparison integrate deep learning techniques, specifically using TensorFlow, with various optimization methods: Genetic Algorithm (GA), Ant Colony Optimization (ACO), LMFIT (employing Nelder-Mead and Powell methods), and Particle Swarm Optimization (PSO). All four approaches aim to predict design parameters for achieving desired response profiles. This analysis examines the methodologies, performance metrics, and unique features of each approach, providing insights into their relative strengths and potential applications.

Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

DOI: 10.55041/ijsrem29303

Author: Saandeep Sreerambatla

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W4403551328

FWCI:

Cites:

Cited by:

Related to: 10