American Journal of Computational and Interdisciplinary Research

E-ISSN: 3069-7093

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

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Volume 2 Issue 6
June 2026

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AI-Driven Computational Models for Multidisciplinary Problem-Solving: Bridging Science and Engineering

Author(s) Baliram Prasad Singh
Country India
Abstract Artificial Intelligence (AI) has evolved from a niche area of computer science into a foundational technology that permeates diverse scientific and engineering disciplines. AI-driven computational models, which combine data-driven learning with domain-specific knowledge, are enabling breakthroughs in areas ranging from biomedical engineering and material science to climate modeling and structural engineering. By leveraging techniques such as deep learning, reinforcement learning, and hybrid AI-physics approaches, researchers can tackle complex, multidisciplinary problems that were previously unsolvable using traditional computational methods alone. This paper explores the transformative potential of AI-driven computational models as a bridge between science and engineering. It analyzes how AI supports predictive modeling, optimization, and simulation across fields, presents case studies from healthcare, energy, and materials research, and discusses challenges such as interpretability, data bias, and integration into existing workflows. The study concludes that AI-driven computational models are not merely tools but catalysts for multidisciplinary collaboration, accelerating innovation and driving more sustainable, efficient, and resilient solutions.
Keywords Artificial Intelligence, Computational Models, Multidisciplinary Research, Engineering, Scientific Problem-Solving, Deep Learning, Simulation, Optimization
Field Engineering
Published In Volume 1, Issue 1, August 2025
Published On 2025-08-02
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