499 papers
The range from Cs to Ce spans a fascinating array of scientific inquiry, bridging diverse disciplines that explore the building blocks of our world. From the intricate chemistry of rare earth elements to the complex dynamics of celestial bodies, this section captures the breadth of modern research where fundamental laws meet practical application. These studies often reveal how matter behaves under extreme conditions or how specific chemical properties drive innovation in technology and medicine.
Gist.Science ensures you never miss a breakthrough by monitoring arXiv for every new preprint in this category. We process each submission as it arrives, transforming dense academic manuscripts into both accessible plain-language overviews and detailed technical summaries. This approach allows you to grasp the core findings quickly while still having the depth needed for serious analysis, all without wading through unfamiliar jargon.
Below are the latest papers in this collection, organized by their release date on arXiv and accompanied by our curated summaries to help you navigate the newest developments.
This review provides a practical introduction to neural operator architectures for solving parametric partial differential equations, evaluating models like DeepONet and Fourier Neural Operators on canonical problems while exploring their application in Bayesian inverse problems and outlining strategies for improving accuracy and generalization in scientific computing workflows.
This paper introduces STORX, an open-source, object-oriented MATLAB framework designed to facilitate the learning and research of shape and topology optimization through a modular, extensible architecture that seamlessly integrates parametric and level-set methods with finite element analysis and visualization.
This paper presents a multi-vector AI security agent for banking that fuses transaction and session data streams using a hybrid architecture of LSTM sequence models, statistical monitors, and graph networks to simultaneously detect signature-based fraud and behavioral financial crimes, achieving significantly higher F1 scores than rule-based or single-model baselines while providing low-latency automated responses and analyst support.
This paper proposes an online spectral deflation strategy that accelerates the solution of parameter-dependent, state-constrained optimal control problems by reusing a single full-domain reference eigenbasis to precondition Krylov subspace solvers on varying inactive sets, achieving significant reductions in iteration counts and wall time across diverse PDE benchmarks.
The paper introduces ANCHOR, an online, instance-aware hybrid framework that stabilizes long-horizon neural operator predictions for time-dependent PDEs by adaptively coupling a pretrained model with a classical numerical solver, using a physics-informed residual estimator to trigger corrective interventions without requiring ground-truth solutions.
This paper introduces a deterministic, interpretable framework for optimizing signal-integrity-compliant PCB interconnects by combining neural surrogate models for waveform prediction, decision trees for physical quality gating, and the Earth Mover's Distance for ranking designs against an ideal reference signal, thereby offering a transparent and efficient alternative to conventional stochastic optimization methods.
This paper introduces BN-LTE, a Bayesian structural framework that integrates latent time embedding and biologically constrained dependencies to model Alzheimer's disease progression, successfully forecasting regional tau changes while identifying a critical mid-pseudotime window of amyloid sensitivity.
This paper presents a high-fidelity, physics-based numerical model of a full-scale test bridge at the University of the Bundeswehr Munich, which was refined using Bayesian updating on experimental data to create a functional digital twin for accurately assessing structural performance under various environmental and damage conditions.
This paper presents a high-fidelity physics-based numerical model of a mixed steel-concrete bridge at the University of the Bundeswehr Munich, utilizing sensitivity analysis within a structural health monitoring framework to identify influential parameters, quantify uncertainty, and enhance prognostic capabilities for aging infrastructure.
This paper proposes "trace-economic underwriting," a framework that quantifies autonomous AI risk at the customer-task-trace level using deterministic economic labels to enable profitable insurance coverage, thereby making autonomous AI deployment economically viable by ensuring expected benefits exceed the costs of premiums, controls, and residual risk.