Shubham Kumar Modi,
- Student, Radha Govind UniversityRamgarh, Jharkhand 1Supervisor, Jhumri Telaiya Municipal Council, Koderm, Jharkhand, India
Abstract
India’s emergence as a global leader in deep-tech innovation is driven by ambitious scientific
megaprojects, including the Laser Interferometer Gravitational-Wave Observatory (LIGO)-India, the
X-ray Polarimeter Satellite (XPoSat), the Aditya-L1 solar observatory, and the National Quantum
Mission (NQM). However, the unprecedented scale and complexity of the observational data generated
by these missions present severe computational bottlenecks. Traditional analytical frameworks
struggle with non-stationary noise transients, diffusion blurring, and the exponential scaling limits
inherent in multidimensional physical systems. This paper proposes a comprehensive, publicationready
machine learning framework specifically engineered for precision physics data reconstruction.
The tripartite framework integrates Physics-Informed Neural Networks (PINNs) and Physics-
Informed Kolmogorov-Arnold Networks (PIKANs) to enforce strict adherence to governing physical
laws; selective State Space Models (SSMs), specifically the Mamba architecture, to achieve lineartime
sequence modeling for continuous telemetry; and Geometric Deep Learning (GDL), including
Hypernetwork-modulated Restricted Boltzmann Machines (HyperRBMs), to preserve spatial
symmetries in non-Euclidean domains. Our analysis demonstrates that the deployment of
reinforcement learning via Deep Loop Shaping successfully reduces optomechanical mirror jitter in
gravitational-wave interferometers by a factor of 30 to 100 compared to classical controllers.
Furthermore, the application of deep convolutional ensembles for X-ray polarimetry reduces required
observation exposure times by approximately 40%. In the quantum domain, parametric Quantum
State Tomography (QST) utilizing GDL achieves over 90% reconstruction fidelity for multi-qubit
systems while completely bypassing the traditional exponential measurement barrier. By transitioning
artificial intelligence from a passive post-processing tool to an integrated, physics-constrained
component of experimental instrumentation, this framework provides the essential computational
scaffolding required to maximize the scientific yield of India’s sovereign deep-tech megaprojects
through 2026 and beyond.
Keywords: Precision physics, scientific machine learning, physics-informed neural networks, state space models, geometric deep learning, gravitational-wave astronomy, x-ray polarimetry, quantum state tomography, deep-tech India, data reconstruction
[This article belongs to Research & Reviews : Journal of Physics ]
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Research & Reviews : Journal of Physics
| Volume | 15 | |
| Issue | 02 | |
| Received | 15/04/2026 | |
| Accepted | 29/05/2026 | |
| Published | 10/06/2026 | |
| Publication Time | 56 Days |