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The aim is to assess how computational mathematics and machine learning can address sustainability challenges through enhanced solutions.

April 8, 2026Open Access

AI Tools For Calculus: Enhancing Algorithmic Discovery And Mathematical Understanding

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The aim is to assess how computational mathematics and machine learning can address sustainability challenges through enhanced solutions.
Overview and evaluation of computational mathematics techniques
Application of machine learning models in environmental and climate modelling
Critique of various computational approaches including PDE frameworks and evolutionary optimization methods
Exploration of concepts like digital twins and quantum-inspired optimization
Addressing challenges in algorithm scalability and model interpretability
Identification of effective computational methods for sustainable engineering innovations
Demonstration of the role of AI in facilitating carbon-neutral system design
Insights into the applications of high-performance computing in climate simulations
Highlighting future research directions in distributed computing and hybrid AI–physics models

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Evaluates computational mathematics techniques to improve sustainability solutions, suggesting enhanced methodologies for global challenges.

Latha et al. (2026) studied this question.

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Evaluates computational mathematics techniques to improve sustainability solutions, suggesting enhanced methodologies for global challenges.