arXiv:2603.25530v1 Announce Type: new
Abstract: Tensors provide a structured representation for multidimensional data, yet discretization can obscure important information when such data originates from continuous processes. We address this limitation…
arXiv:2603.23658v1 Announce Type: new
Abstract: Gradient boosting, a method of building additive ensembles from weak learners, has established itself as a practical and theoretically-motivated approach to approximate functions, especially using decisi…
arXiv:2603.23799v1 Announce Type: new
Abstract: Physics-informed neural networks (PINNs) are increasingly used in mathematical epidemiology to bridge the gap between noisy clinical data and compartmental models, such as the susceptible-exposed-infecte…
arXiv:2603.24143v1 Announce Type: new
Abstract: Neural operator learning directly constructs the mapping relationship from the equation parameter space to the solution space, enabling efficient direct inference in practical applications without the ne…
arXiv:2507.07580v3 Announce Type: replace
Abstract: Recent studies suggest that context-aware low-rank approximation is a useful tool for compression and fine-tuning of modern large-scale neural networks. In this type of approximation, a norm is weigh…
arXiv:2603.24594v1 Announce Type: new
Abstract: We introduce the Multilevel Euler-Maruyama (ML-EM) method compute solutions of SDEs and ODEs using a range of approximators $f^1,\dots,f^k$ to the drift $f$ with increasing accuracy and computational cos…
arXiv:2512.03923v2 Announce Type: replace
Abstract: In this paper, we adapt the Discrete Variable (DV)-Circuit Quantum-Classical Physics-Informed Neural Network (QCPINN) and apply it for the first time to four typical reservoir seepage models. These i…