Department of
Physics

Chandrakala Meena received her BS-MS Dual Degree (2009–2014) and PhD (2014-2018) in Physics from the IISER Mohali. She then pursued postdoctoral research at Bar-Ilan University, Israel, from August 2018 to October 2020. Following this, she returned to India as an INSPIRE Faculty Fellow at CSIR-NCL Pune, where she worked from November 2020 to December 2022. Prior to joining IISER Pune as an Assistant Professor Grade 1, she served as an Assistant Professor Grade 1 in the School of Physics at IISER Thiruvananthapuram from December 2022 to May 2025.

Research

Nonlinear Dynamics, Complex Networks, Machine Learning

Dr. Chandrakala Meena’s research focuses on the theoretical and computational understanding of complex systems across diverse domains, including biology, climate science, social dynamics, and engineering networks. Her work addresses fundamental questions about how macroscopic behaviours emerge from microscopic interactions in large-scale systems, and how these behaviours can be predicted, stabilized, or controlled using mathematical and computational tools.

At the core of her research is the development of frameworks that integrate nonlinear dynamics, network theory, data-driven modelling, and machine learning to study emergent properties in complex systems. A key focus is on understanding how the interplay between network topology (the structural pattern of interconnections) and local dynamics (the evolution rules of individual elements) shapes the macroscopic behaviour of the system. Her work also investigates how to determine the stability of these emergent behaviours and control undesired dynamics toward desired targeted outcomes.

Her research also explores also explores the integration of machine learning and artificial intelligence with nonlinear dynamics and network science theories to extract hidden patterns, forecast critical transitions, and infer governing equations directly from data. These approaches are especially relevant for high-dimensional or poorly understood systems where traditional modelling is difficult.

In summary, her research combines analytical theory, numerical simulations, empirical datasets, and AI-based inference methods, her research provides deep theoretical insights and practical tools to address challenges in complex systems. Her contributions have broad applicability in scientific research, technological innovation, and policy-oriented decision-making, helping to build predictive and controllable frameworks for complex real-world systems.

Selected Publications

Dheeraja Thakur, Athul Mohan, G. Ambika and Chandrakala Meena, “Machine learning approach to detect dynamical states using recurrence measures", Chaos: An Interdisciplinary Journal of Nonlinear Science 34 (4), 2024.

Chandrakala Meena, Chittaranjan Hens, Suman Acharyya, Simi Haber, Stefano Boccaletti and Baruch Barzel, “Emergent stability in complex network dynamics”, NATURE Physics, 43, 2023.

Chandrakala Meena, Pranay Deep Rungta and Sudeshna Sinha, “Resilience of networks of multi-stable chaotic systems to targetted attacks”, The European Physical Journal B, 93 (11), 1-9, 2020.

Chandrakala Meena, Pranay Deep Rungta and Sudeshna Sinha, “Threshold-activated transport stabilizes chaotic populations to steady states”, PLoS ONE, 12(8):e0183251, 2017.

Chandrakala Meena, Shweta Kumari, Akansha Sharma and Sudeshna Sinha, “Effect of Heterogeneity in Models of El Niño Southern Oscillations”, Chaos, Solitons and Fractals, 104:668-679, 2017.