Principal Investigator · Theoretical and computational physicist
Developing a physics-grounded account of adaptive organisation across active matter, complex systems, neurophysiological signals and physics-inspired computation.
Research programme
Dr. Beuria studies many-body systems whose motion, interactions and response rules are modulated by internal states, feedback, history and environmental context.
The programme seeks predictive explanations of adaptive collective behaviour by treating memory, nonreciprocity and geometric constraints as constitutive parts of the dynamics. This creates a coherent route from active and biological matter to geometric diagnostics, neural signals and dynamical computation.
Across these settings, a recurring question is how an adaptive system reorganises its state space under changing physical, cognitive or contextual conditions—and which mathematical observables make that reorganisation interpretable.
Research streams
Formal physics, geometric data analysis and biological signals inform one another across the programme.
Physics of living systems
Memory, feedback, context and nonreciprocal couplings are treated as physical degrees of freedom that reshape the organisation of active agents, biological aggregation and other adaptive many-body systems.
Neuroimaging and AI
EEG, HRV and multimodal physiological data are analysed with persistent homology, Hodge spectral entropy, graph descriptors and deep learning to study meditation, attention, agency, cognitive load and affect.
Consciousness and perception
Category-theoretic and topological approaches are used to clarify phenomenal unity, intersubjective coherence and part–whole relations while keeping philosophical interpretation distinct from empirical evidence.
Core themes
Non-Markovian agents, internal variables, hysteresis and path-dependent collective transitions.
Directed effective interactions that emerge from structured contexts and geometric constraints.
Persistent homology, Ricci curvature, graph Laplacians and Hodge tools as interpretable order parameters.
Multi-timescale reservoirs and physics-constrained architectures for temporal learning and adaptive coordination.
Research trajectory
The current trajectory develops formal and computational approaches to collective organisation, memory and contextual dynamics.
Topology and discrete geometry provide interpretable diagnostics that move across adaptive matter, biological aggregation and neural signals. They allow changes in high-dimensional state spaces to be studied without reducing the analysis to opaque predictive features.
The programme brings these strands together: memory, context, nonreciprocity and topology are studied as co-evolving features of adaptive physical systems, with carefully bounded extensions to cognition, consciousness and computation.
Selected publications
Journal articles and current preprints. Every entry includes its complete author list and persistent DOI where available.
Scientific Reports
https://doi.org/10.1038/s41598-026-65824-7Physical Review E 113, 065401
https://doi.org/10.1103/kn6c-nb4lProceedings of the Royal Society A 481, 20250489
https://doi.org/10.1098/rspa.2025.0489Royal Society Open Science 11, 240794
https://doi.org/10.1098/rsos.240794Biomedical Signal Processing and Control 89, 105779
https://doi.org/10.1016/j.bspc.2023.105779arXiv:2510.23688 · revised 2026
https://doi.org/10.48550/arXiv.2510.23688Academic background
Homi Bhabha National Institute; research at the Harish-Chandra Research Institute, Prayagraj.
Indian Institute of Technology Mandi, with research spanning complex systems, neuroimaging and applications of AI.
Leads the IKS Research Centre for Philosophical and Cognitive Sciences hosted by ISS Delhi.
Research contact
For centre-related research enquiries, contact CPCSci or consult Dr. Beuria’s full academic website.
Earlier scholarly record
This earlier work is part of Dr. Beuria’s individual publication history and is not a CPCSci research focus.
arXiv:2509.19756 · under review
https://doi.org/10.48550/arXiv.2509.19756arXiv:2510.10900 · under review
https://doi.org/10.48550/arXiv.2510.10900Physical Review D 110, 035023
https://doi.org/10.1103/PhysRevD.110.035023Physics Letters B 846, 138188
https://doi.org/10.1016/j.physletb.2023.138188Computer Physics Communications 226, 187–205
https://doi.org/10.1016/j.cpc.2017.12.021Journal of High Energy Physics 2017(11)
https://doi.org/10.1007/JHEP11(2017)042Journal of High Energy Physics 2017(10)
https://doi.org/10.1007/JHEP10(2017)154Journal of High Energy Physics 2017(4)
https://doi.org/10.1007/JHEP04(2017)024Journal of High Energy Physics 2016(8)
https://doi.org/10.1007/JHEP08(2016)004Journal of High Energy Physics 2015(9)
https://doi.org/10.1007/JHEP09(2015)073