International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 08 | Aug 2025
p-ISSN: 2395-0072
www.irjet.net
Data-Driven Approaches in Computational Science: Integrating AI with Mathematics and Physics M Devuja1, B Jagan Mohan2, S Sai Teja3 1Asst Prof, Dept. of Physics, Priyadarshini Govt Degree College for Women, Gadwal, Telangana, India
2Lecturer, Dept. of Mathematics, Priyadarshini Govt Degree College for Women, Gadwal, Telangana, India 3Lecturer, Dept. of Computer Science, Priyadarshini Govt Degree College for Women, Gadwal, Telangana, India ---------------------------------------------------------------------***--------------------------------------------------------------------ABSTRCT INTRODUCTION
Computational science has transformed into a dynamic, multidisciplinary field that draws on advanced techniques from artificial intelligence (AI), mathematics, chemistry, and physics to drive scientific discovery and problemsolving. By adopting data-driven approaches, researchers can integrate complex datasets, build predictive models, and automate processes leading to major breakthroughs across a wide range of disciplines. Mathematics underpins the field by providing essential algorithms for numerical methods, optimization, and statistical analysis, ensuring accuracy and rigor in data interpretation. Physics contributes fundamental principles that govern real-world behavior, enabling precise simulations in areas like quantum mechanics, fluid dynamics, and thermodynamics. Chemistry adds depth through molecular modelling and reaction kinetics, playing a critical role in drug discovery, materials science, and environmental studies. AI enhances all of these areas by efficiently analyzing large datasets, uncovering patterns, and generating predictive insights, significantly boosting the decision-making capabilities of computational research. This paper delves into how AIpowered, data-driven methodologies accelerate simulations, deepen scientific understanding, and enhance accuracy across mathematical, physical, and chemical applications. Techniques such as machine learning, neural networks, and high-performance computing allow researchers to tackle complex problems with greater speed and efficiency. Moreover, interdisciplinary collaboration is driving innovation in areas like healthcare, clean energy, and advanced material development. The fusion of AI and computational science marks a major shift in how research is conducted streamlining workflows, enabling automated discovery, and opening new frontiers. As computational methods continue to evolve, the integration of data science with mathematics, chemistry, and physics will reshape scientific exploration and unlock groundbreaking advancements across multiple domains.
Human life is a continuous journey of learning, shaped by observation and experience. Through these, we gather different types of information—some measurable, others more descriptive. By repeatedly witnessing events, we start to recognize patterns that link data to events and events to one another. In scientific discovery, these patterns are formalized into laws and equations, while data is represented through properties and variables. Observations, whether actions or characteristics, are measured to help understand these relationships. Laws and equations, common in science, enable us to make predictions and efficiently share knowledge with minimal information. However, traditional scientific learning is slow. It requires extensive observation, often through costly experiments, to identify key variables and their effects across numerous possible scenarios—sometimes overlooking important, unexpected factors. Additionally, science typically relies on hypotheses, meaning it carries an inherent bias. The scientific method was developed to counteract the natural biases and limitations of human thinking, particularly our tendency to seek explanations beyond observable reality.
Keywords: AI, Mathematics, Physics, Chemistry, Data
Fig:1 Difference B/W Paradigm approach
Sets, Interdisciplinary Sciences, Quantum Computing.
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