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Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization Non-normal spectral signatures of instability in neural network training dynamics Optimization of randomized neural networks for transfer operator approximation Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics Learning Decision-Sufficient Representations for Linear Optimization Parameterized Complexity of Stationarity Testing for Piecewise-Affine Functions and Shallow CNN Losses Prabhakar function and unified fractional kinetic equation in bicomplex space Computing Gamma(p/q) with Beta function values Flows on Graded Manifolds Optimal embedding dimension in the Nash--Tognoli theorem An optimal first-order method for smooth and strongly convex composite optimization and its stationary limit Sharp Bohr-Type inequalities for certain classes of close-to-convex functions Invariants of real affine varieties based on their complexifications Topological symmetric and braid homologies A Formal Graph-Theoretic Framework for Pitch Class Set Analysis Finite groups with high commuting probability for Sylow subgroups Performance Bounds for Rollout Policies in Stochastic Shortest Path Problems Real 2-blocks in quasi-simple groups Maximal subalgebras of the Lie algebra $W_n(\mathbb{K})$ Cohomogeneity-One Ruled Hypersurfaces in $\mathbb{CP}^2$ and $\mathbb{C}H^2$ Global analysis of the Kuramoto flow Cartier algebras through the lens of $p$-families Positivity in the context of Hodge modules and Higgs bundles on Deligne-Mumford stacks A secondary pairing between K-theory and K-homology, relative eta invariants, and zeta maps Detecting and Correcting Sample-by-Sample Scale Distortion in RNA Sequencing Data Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Training-Free Looped Transformers Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Asymmetric Scaling Laws from Sparse Features Is Dimensionality a Barrier for Retrieval Models? 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Magnetic Resonance Dynamics via Fractional Bloch Equation: a Hybrid Computational Framework
[Submitted on 29 May 2026] · 2026-06-01 · via math updates on arXiv.org

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Abstract:Bloch equations are a powerful tool in describing the dynamics of nuclear magnetization in magnetic resonance phenomena. The fractional generalization of the Bloch equation effectively captures the anomalous relaxation and diffusion in porous, heterogeneous, and complex media. These equations describe how nuclear magnetization evolves under the influence of magnetic fields and relaxation processes. This work effectively employs a hybrid approach, the Laplace residual power series method, to investigate and analyze the fractional Bloch equation. A series solution is derived as the approximate solution for magnetization components. The influence of fractional order on each magnetization component in magnetization dynamics is analyzed and illustrated graphically. We conduct an error analysis to demonstrate the reliability and effectiveness of the proposed approach. The superiority of the suggested approach is shown using a comparative study with existing methods. The findings indicate the potential of the suggested approach as a reliable tool in understanding fractional magnetic resonance systems arising in applications such as NMR spectroscopy, MRI, MRF, and other complex heterogeneous materials.

Submission history

From: Neetu Garg Dr [view email]
[v1] Fri, 29 May 2026 05:27:56 UTC (775 KB)