Chaos-Informed Learning: Teaching Machines to Predict the Unpredictable

Author(s)	Subhasis Kundu
Country	United States
Abstract	This paper explores the innovative concept of Chaos-Informed Learning, a novel approach to machine learning that leverages principles of chaos theory to predict ostensibly unpredictable events. By analyzing patterns within chaotic systems, we demonstrate how this methodology can be employed to forecast market crashes, pandemics, and other global disruptions. Our research integrates advanced machine learning algorithms with chaos theory to identify early warning signals and potential tipping points in complex systems [1]. Through comprehensive simulations and real-world case studies, we illustrate the efficacy of Chaos-Informed Learning in enhancing predictive capabilities across various domains. The findings indicate a significant improvement in forecasting accuracy compared to traditional methods, particularly in contexts characterized by high uncertainty and non-linear dynamics. This groundbreaking approach holds substantial implications for risk management, policy-making, and strategic planning in an increasingly unpredictable world.
Keywords	Chaos Theory, Machine Learning, Predictive Analytics, Complex Systems, Global Disruptions, Risk Management, Nonlinear Dynamics.
Field	Engineering
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-10-23
DOI	https://doi.org/10.71097/IJSAT.v16.i4.9031
Short DOI	https://doi.org/g97q36

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology