Korzhinskii-Net: Physics-Informed Neural Network for Sub-Surface Mineral Prospec
arXiv:2603.25779v1 Announce Type: cross Abstract: Groundwater represents a key element of the water cycle, yet it exhibits intricate and context-dependent relationships that make
arXiv:2608.11282v1 Announce Type: cross Abstract: Quantifying myocardial perfusion from cardiac magnetic resonance (CMR) can be achieved by fitting tracer-kinetic models to the
arXiv:2604.26999v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) approximate solutions of partial differential equations (PDEs) by embedding physical laws
arXiv:2607.06479v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide a promising framework for solving partial differential equations while embedding
arXiv:2604.15392v1 Announce Type: cross Abstract: Physics-Informed Neural Networks (PINNs) often suffer from slow convergence, training instability, and reduced accuracy on
arXiv:2609.02060v1 Announce Type: new Abstract: Mineral exploration requires integrating heterogeneous geochemical, geophysical, and geological evidence, yet existing
arXiv:2605.04074v1 Announce Type: cross Abstract: AI data centers experience rapid fluctuations in power demand due to the heterogeneity of computational tasks that they have to
arXiv:2603.22309v1 Announce Type: cross Abstract: Partial differential equation (PDE) simulation holds extensive significance in scientific research. Currently, the integration of
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