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Anshuman Sahoo

Publications and source records attributed to Anshuman Sahoo.

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Enabling Electrical Readout of Néel vector reversal in a van der Waals Antiferromagnet

Owing to its robustness against external perturbations and intrinsically ultrafast dynamics, the Néel vector in antiferromagnets (AFMs) can enable the development of next-generation spintronic and magnonic devices for memory and computing applications. To realize AFM-based magnetic memory devices, one of the key requirements is to demonstrate electrical readout of 180-degree reversal of Néel vector in thin film AFMs, which remains critically missing. In this work, we report experimental demonstration of a novel transport methodology to detect Néel vector reversal in atomically thin films of a van der Waals (vdW) based A-type AFM. For this, we utilize spin-dependent electronic band properties of CrSBr by coupling it to a spin-polarized layer, separated by a tunnel barrier. In this configuration, the spin-dependent tunnelling magnetoresistance (MR) becomes sensitive to the relative orientation between the magnetization of the reference electrode and the interfacial sublattice magnetization of the AFM layer, in turn enabling electrical detection of the Néel vector orientation. Importantly, the observed MR can also reveal 180-degree reversal of Néel vector in even-layers of CrSBr, wherein adjacent sublattice magnetic layers are exactly compensated and the net magnetization vanishes and thus establishes a broadly applicable strategy for electrical detection of Néel vector in vdW-based AFMs.

cond-mat.mes-hall

A Geomechanically-Informed Framework for Wellbore Trajectory Prediction: Integrating First-Principles Kinematics with a Rigorous Derivation of Gated Recurrent Networks

Accurate wellbore trajectory prediction is a paramount challenge in subsurface engineering, governed by complex interactions between the drilling assembly and heterogeneous geological formations. This research establishes a comprehensive, mathematically rigorous framework for trajectory prediction that moves beyond empirical modeling to a geomechanically-informed, data-driven surrogate approach.The study leverages Log ASCII Standard (LAS) and wellbore deviation (DEV) data from 14 wells in the Gulfaks oil field, treating petrophysical logs not merely as input features, but as proxies for the mechanical properties of the rock that fundamentally govern drilling dynamics. A key contribution of this work is the formal derivation of wellbore kinematic models, including the Average Angle method and Dogleg Severity, from the first principles of vector calculus and differential geometry, contextualizing them as robust numerical integration schemes. The core of the predictive model is a Gated Recurrent Unit (GRU) network, for which we provide a complete, step-by-step derivation of the forward propagation dynamics and the Backpropagation Through Time (BPTT) training algorithm. This detailed theoretical exposition, often omitted in applied studies, clarifies the mechanisms by which the network learns temporal dependencies. The methodology encompasses a theoretically justified data preprocessing pipeline, including feature normalization, uniform depth resampling, and sequence generation. Trajectory post-processing and error analysis are conducted using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R2).

physics.geo-ph