Theoretical and computational physics
I study adaptive non-equilibrium systems where memory, geometry, context, and nonreciprocal interactions shape collective organization far from equilibrium.
My long-term program develops a physics-grounded theory of adaptive active matter: many-body systems whose motion, interactions, and response rules are modulated by internal states, feedback, history, and environmental context.
The goal is to make adaptive collective behavior predictive by treating memory, feedback, nonreciprocity, and geometric constraints as physical degrees of freedom rather than phenomenological additions. This creates a route from active and biological matter to geometric diagnostics, neurophysiological signals, and physics-constrained computation.
This is the center of my current research program. I study how internal memory, feedback, context, and nonreciprocal couplings reshape the collective dynamics of active agents, biological aggregation, and high-dimensional adaptive systems. The work combines non-equilibrium statistical physics, dynamical systems, graph geometry, topological data analysis, and quantum-inspired models of decision-like collective motion.
EEG, HRV, and multimodal physiological signals provide a biological testbed for the same broader question: how does an adaptive system reorganize its internal state space under changing cognitive or affective conditions? My work in this stream uses persistent homology, Hodge spectral entropy, graph-theoretic descriptors, and deep learning to study meditation, cognitive load, agency, attention, odor perception, and non-stationary neural dynamics.
My doctoral foundation was in high-energy phenomenology, supersymmetric model-building, vacuum structure, and collider event analysis. That training in field-theoretic modeling, numerical simulation, and high-dimensional data analysis later became the technical bridge to my current use of topology and discrete geometry as interpretable diagnostics for complex physical data.
Non-Markovian agents with internal dynamical variables, hysteresis, path-dependent transitions, and anomalous relaxation.
Directed interactions emerging from structured contexts rather than imposed asymmetric rules, with consequences for stability and collective modes.
Persistent homology, Ricci curvature, graph Laplacians, and Hodge tools as interpretable order parameters for complex phase spaces.
Active-matter-inspired reservoirs, nonreciprocal graph topologies, and physics-constrained architectures for temporal learning tasks.
My research trajectory begins with rigorous theoretical physics and moves steadily toward a unified physics of adaptive organization. During my PhD at Harish-Chandra Research Institute under HBNI, I worked on high-energy phenomenology, supersymmetric model spaces, charge and color breaking vacua, collider signatures, and high-dimensional event analysis. That period trained me to build models, simulate complex systems, and extract structure from large phase spaces.
After this foundation, I shifted toward complex systems, computational neuroscience, and the physics of adaptive organization through independent research initiatives and collaborations. The bridge was geometry: I began treating persistent homology, Forman-Ricci curvature, graph structure, and topological summaries as intrinsic order parameters for physical data rather than as black-box machine-learning features.
That geometric bridge now supports my current program on adaptive active matter, where memory, nonreciprocity, contextual modulation, and topology are treated as co-evolving physical degrees of freedom. The resulting arc runs from collider phase spaces to biological aggregation, quantum-inspired collective motion, non-Markovian active matter, neurophysiological signals, and eventually physics-constrained computation.
This work gives a concrete mathematical mechanism for how nonreciprocal interactions can emerge when agents move through a structured context manifold. Instead of imposing asymmetric forces by hand, the model shows how contextual geometry can generate directed effective couplings, circulating probability currents, coherent modes, and collective responses without an equilibrium analogue.
Beuria, J., & Chembrolu, V. H. (2026). Topological flux on a context manifold generates nonreciprocal collective dynamics. Physical Review E, 113(6), 065401. doi:10.1103/kn6c-nb4l
This paper develops a quantum-inspired decision process for active matter, linking perception, uncertainty, and collective motion. It expands standard active-matter models by allowing internal perceptual dynamics to modulate how agents align, respond, and reorganize.
Beuria, J., Chaurasiya, M., & Behera, L. (2025). Collective motion from quantum-inspired dynamics in visual perception. Proceedings of the Royal Society A, 481(2321), 20250489. doi:10.1098/rspa.2025.0489
This work introduces discrete Ricci curvature as a driver and diagnostic of biological aggregation. It uses graph geometry to model how non-local interactions can reshape aggregation patterns, providing a bridge between biophysical organization and interpretable geometric structure.
Beuria, J., & Behera, L. (2024). Non-local interaction in discrete Ricci curvature-induced biological aggregation. Royal Society Open Science, 11(9), 240794. doi:10.1098/rsos.240794
This manuscript studies active collectives with slow internal variables that self-regulate fast spatial motion. The central question is how history, multi-timescale regulation, and memory alter synchronization, phase transitions, hysteresis, and recovery after perturbation.
Beuria, J. (2025). Non-Markovian collective motion from self-regulated perceptual dynamics. arXiv:2510.23688.
This work explores context-sensitive state-space reorganization in large language models. Although the substrate differs from active matter, the conceptual question is shared: how does context reshape transition structure, effective probability, and collective response in adaptive systems?
Beuria, J. (2026). Emergent non-classical probabilistic structure in large language models under contextual modulations. SSRN.
This framework brings the language of context, coherence, and state-space organization to the study of consciousness. It aims to formalize how unified experience and shared coherence can be discussed without reducing them to loose metaphor, connecting philosophical questions with mathematically structured models of adaptive systems.
Beuria, J., Chembrolu, V. H., & Kumar, N. (2026). A formal framework for phenomenal unity, contextual experience, and intersubjective coherence. PhilPapers.
This study applies topological data analysis and Hodge spectral entropy to EEG signals from meditative states. It treats neural activity as a complex, noisy, non-stationary signal whose structure can be described through topology rather than only through conventional spectral features.
Gupta, K. V., Beuria, J., & Behera, L. (2024). Characterizing EEG signals of meditative states using persistent homology and Hodge spectral entropy. Biomedical Signal Processing and Control, 89, 105779. doi:10.1016/j.bspc.2023.105779
This work uses deep neural networks to decode cognitive-load changes from physiological signals. It sits at the interface of contemplative practice, signal processing, and machine learning, asking how cognitive transitions can be detected in complex multimodal data.
Singh, S., Beuria, J., Behera, L., Pachori, R. B., & Gupta, K. V. (2026). Decoding cognitive load changes induced by mantra meditation from physiological signals using deep neural network. IEEE Sensors Journal, 26(1), 1088-1102. doi:10.1109/JSEN.2025.3628683
This pilot study examines agency and self-reflection through neural correlates, connecting subjective structure with measurable brain dynamics. It is part of the broader attempt to make cognitive and experiential transitions accessible to quantitative analysis.
Gupta, K. V., Beuria, J., Vijanapalli, L. K., Sethi, A., & Behera, L. (2025). Self-reflection, sense of agency, and underlying neural correlates: A pilot study. PLOS ONE, 20(12), e0335276. doi:10.1371/journal.pone.0335276
This manuscript combines olfactory EEG with physicochemical descriptors of odorants to predict perception. It extends the neuro-AI program into multimodal sensory integration, where brain signals and stimulus structure must be modeled together.
Ameta, D., Singh, L., Beuria, J., & Behera, L. (2026). Multimodal odor perception prediction using olfactory EEG and physicochemical features of odorants. ChemRxiv. doi:10.26434/chemrxiv-2025-fbmlf/v2
This work studies EEG markers in real and immersive environments using persistent homology of multivariate neural dynamics. It asks how affective and contemplative states reorganize the geometry of brain activity.
Singh, S., Beuria, J., Pachori, R. B., & Behera, L. (2026). Neural and affective markers of kirtan meditation: Persistent homology of multivariate EEG in real and immersive environments. Manuscript under review.
This manuscript uses topology and graph-based descriptors to compare mind-wandering and focused attention. It continues the theme of treating cognitive state changes as reorganizations of a high-dimensional dynamical system.
Beuria, J. (2025). Topological and graph-theoretic analysis of EEG dynamics in mind-wandering and focused attention. Manuscript.
This book chapter synthesizes neuroscience, meditation, and biological measures of well-being, placing empirical signal analysis within a broader account of contemplative practice and human flourishing.
Gupta, K. V., Beuria, J., & Behera, L. (2026). Neuroscience of meditation for holistic well-being. In Biological Measures of Well-Being (pp. 175-188). Academic Press. doi:10.1016/B978-0-443-28842-5.00001-6
This paper uses persistent homology to identify topological structure in collider observations. It marked a key bridge in my trajectory: topology became a way to read high-dimensional physics data intrinsically, not just as an auxiliary feature-engineering technique.
Beuria, J. (2023). Persistent homology of collider observations: When (w) hole matters. Physics Letters B, 846, 138188. doi:10.1016/j.physletb.2023.138188
This work develops Forman-Ricci curvature as an interpretable diagnostic for collider phase spaces. It shows how discrete geometry can expose structure in event data and later informed my use of graph curvature in living and active systems.
Beuria, J. (2024). Intrinsic geometry of collider observations and Forman Ricci curvature. Physical Review D, 110(3), 035023. doi:10.1103/PhysRevD.110.035023
This manuscript combines topological data analysis and machine learning for invisible-decay searches at a future muon collider, extending the geometric collider-data program to beyond-standard-model signatures.
Beuria, J. (2025). Probing invisible decay of Z' at muon collider with topological data analysis and machine learning. arXiv:2509.19756.
This work applies topological reasoning to the structure of beyond-standard- model Higgs-sector data, continuing the effort to make geometry a practical language for high-dimensional particle-physics landscapes.
Beuria, J. (2025). Topological landscapes of the BSM Higgs sector. arXiv:2510.10900.
This PhD-era work studies collider phenomenology in minimal universal extra dimensions, combining model implementation, event-level simulation, and LHC search interpretation.
Beuria, J., Datta, A., Debnath, D., & Matchev, K. T. (2018). LHC collider phenomenology of minimal universal extra dimensions. Computer Physics Communications, 226, 187-205. doi:10.1016/j.cpc.2017.12.021
This paper analyzes charge and color breaking vacuum constraints in a non-holomorphic extension of the MSSM, contributing to the study of viable supersymmetric parameter spaces.
Beuria, J., & Dey, A. (2017). Exploring charge and color breaking vacuum in non-holomorphic MSSM. Journal of High Energy Physics, 2017(10), 154. doi:10.1007/JHEP10(2017)154
This work studies the possibilities and implications of charge-breaking vacua in supersymmetric models, sharpening the theoretical constraints on phenomenologically viable scenarios.
Beuria, J., & Datta, A. (2017). Spontaneous breakdown of charge in the MSSM and in the NMSSM: Possibilities and implications. Journal of High Energy Physics, 2017(11), 42. doi:10.1007/JHEP11(2017)042
This paper examines vacuum viability in the Z3-symmetric NMSSM, combining theoretical consistency with phenomenological constraints in a high-dimensional model space.
Beuria, J., Chattopadhyay, U., Datta, A., & Dey, A. (2017). Exploring viable vacua of the Z3-symmetric NMSSM. Journal of High Energy Physics, 2017(4), 24. doi:10.1007/JHEP04(2017)024
This study analyzes sbottom signatures in natural NMSSM scenarios at the LHC, connecting supersymmetric spectra with collider-search strategies.
Beuria, J., Chatterjee, A., & Datta, A. (2016). Sbottoms of natural NMSSM at the LHC. Journal of High Energy Physics, 2016(8), 4. doi:10.1007/JHEP08(2016)004
This early PhD work investigates LHC signatures of two light stop states in the NMSSM, contributing to the phenomenology of natural supersymmetric spectra.
Beuria, J., Chatterjee, A., Datta, A., & Rai, S. K. (2015). Two light stops in the NMSSM and the LHC. Journal of High Energy Physics, 2015(9), 73. doi:10.1007/JHEP09(2015)073
My biophysics direction treats living systems as adaptive non-equilibrium collectives. Cells, tissues, animal groups, neural systems, and synthetic active agents all exhibit organization shaped by memory, feedback, context, and geometry. In this view, the relevant physical variables include not only position, velocity, density, and alignment, but also internal state, delayed response, history, and the structured environment in which agents interpret signals.
The aim is to derive testable signatures of living organization: delayed recovery after perturbation, path-dependent transitions, circulating probability currents, coherent modes, topology-driven phase-space reorganization, and stability changes generated by context-dependent nonreciprocity.
The formal framework on phenomenal unity, contextual experience, and intersubjective coherence extends the same mathematical sensibility to consciousness research. The central idea is to move from descriptive language to structured models: experience is approached through context, coherence, unity, state-space organization, and relations between individual and shared dynamics.
This work connects with my neuroimaging studies of meditation, agency, self-reflection, and attention. It asks how subjective transitions might be linked to measurable reorganization in neural dynamics, while remaining careful about the distinction between philosophical interpretation and empirical signal structure.
manasai.tech
Brainwave Science Pvt. Ltd.
Indian Institute of Technology Mandi, Himachal Pradesh
IKS Research Center, ISS Delhi; recognized by the IKS Division, Ministry of Education, Government of India.
Homi Bhabha National Institute; Harish-Chandra Research Institute, Prayagraj
Indian Institute of Technology Roorkee
Ravenshaw Junior College, Cuttack
Saraswati Vidya Mandir, Salipur, Cuttack
Kurusetti Vinay Gupta, Jyotiranjan Beuria, and Laxmidhar Behera. "Chapter 14 - Neuroscience of Meditation for Holistic Well-Being." Biological Measures of Well-Being, edited by Colin R. Martin, Vinood B. Patel, Rajkumar Rajendram, and Victor R. Preedy, Academic Press, 2026, pp. 175-188. doi:10.1016/B978-0-443-28842-5.00001-6