A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This paper applies reactor theory to supply chain management.
The paper tackles inverse uncertainty quantification in neutron noise analysis.
Machine learning helps create accurate models of neutron star postmerger signals.
The region of heavy calcium isotopes forms the frontier of experimental and theoretical nuclear structure research where the basic concepts of nuclear physics are put to stringent test. The recent discovery of the extremely neutron-rich nuclei around Ca [Tarasov, 2018] and the experimental determination of masse…
Active learning improves neutron spectroscopy experiments by automating measurement selection.
ANNs predict SAFARI-1 neutron fluxes with uncertainties.
This paper presents a proof-of-concept demonstration of triaxial strain tomography from Bragg-edge neutron imaging within a three-dimensional sample. Bragg-edge neutron transmission can provide high-resolution images of the average through thickness strain within a polycrystalline material. This poses an associated ric…
Gravitational waves are predicted by the general theory of relativity. In [6] D. Christodoulou showed that gravitational waves have a nonlinear memory. We proved in [3] that the electromagnetic field contributes at highest order to the nonlinear memory effect of gravitational waves. In the present paper, we study this …
In X-ray binary star systems consisting of a compact object that accretes material from an orbiting secondary star, there is no straightforward means to decide if the compact object is a black hole or a neutron star. To assist this classification, we develop a Bayesian statistical model that makes use of the fact that …
Young isolated neutron stars (INS) most commonly manifest themselves as rotationally powered pulsars (RPPs) which involve conventional radio pulsars as well as gamma-ray pulsars (GRPs) and rotating radio transients (RRATs). Some other young INS families manifest themselves as anomalous X-ray pulsars (AXPs) and soft gam…
Recently, several algorithms for strain tomography from energy-resolved neutron transmission measurements have been proposed. These methods assume that the stress-free lattice spacing is a known constant limiting their application to the study of stresses generated by manufacturing and loading methods that do not…
The R-function theory of Thomas is used to model neutron inelastic scattering and the fine, intermediate, and gross structure observed in the Dow Jones Industrial Average on a typical trading day.
Paper uses autoencoders for efficient reduced-order modeling of eigenvalue problems.
The main task in oil and gas exploration is to gain an understanding of the distribution and nature of rocks and fluids in the subsurface. Well logs are records of petro-physical data acquired along a borehole, providing direct information about what is in the subsurface. The data collected by logging wells can have si…
New method finds all thin film structures from reflectometry data.
Accelerates pulsar light curve inference with learned representations and optimization.
Inverse problems are pervasive mathematical methods in inferring knowledge from observational and experimental data by leveraging simulations and models. Unlike direct inference methods, inverse problem approaches typically require many forward model solves usually governed by Partial Differential Equations (PDEs). Thi…
Recently, it has been shown that Absolute Parallelism (AP) geometry admits paths that are naturally quantized. These paths have been used to describe the motion of spinning particles in a background gravitational field. In case of a weak static gravitational field limits, the paths are applied successfully to interpret…
Quantified limits of nuclear stability beyond drip lines.
New method uses SBI to infer magnetorotational properties of isolated pulsars.
Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due to the nonlinear and heterogeneous nature of the subsurface physical properties. In this context, present study proposes a generalized one cl…
New methods estimate transport-growth pairs in unbalanced optimal transport.
Stationary and axially symmetric space-times play an important role in astrophysics, particularly in the theory of neutron stars and black holes. The static vacuum sub-class of these space-times is known as Weyl's class, and contains the Schwarzschild space-time as its most prominent example. This paper is going to stu…
Introduces statistical optimal transport for probabilistic lectures.
Study shows how optimal transport behaves in higher dimensions.
A concise discussion of the axiomatic approach to the concept of parallel transport is presented. Attention is drawn to a bijective map between the sets of connections and (axiomatically defined) parallel transports. The transports along paths are pointed as a generalization of the (axiomatically defined) parallel tran…
The axiomatic approach to parallel transport theory is partially discussed. Bijective correspondences between the sets of connections, (axiomatically defined) parallel transports, and transports along paths satisfying some additional conditions, are constructed. In particular, the equivalence between the concepts "conn…
New framework uses cohomology to analyze probabilistic distortions and arbitrage.
Under mild regularity assumptions, the transport problem is stable in the following sense: if a sequence of optimal transport plans converges weakly to a transport plan , then is also optimal (between its marginals). Alfonsi, Corbetta and Jourdain asked whether the same property is true for th…
NOT learns optimal transport plans, kernel costs improve performance.
New algorithm solves unbalanced optimal transport on trees in quasi-linear time.
A review of the parallel transport (translation) in fibre bundles is presented. The connections between transports along paths and parallel transports in fibre bundles are examined. It is proved that the latter ones are special cases of the former.
Using McCann's transportation map, we establish a transport inequality on compact manifolds with positive Ricci curvature. This inequality contains the sharp spectral comparison estimates.
New metric for probability measures connects physics and geometry.
A new algorithm for parallel transport on shape spaces is presented and compared to existing methods.
Extends optimal transport to dynamic and martingale settings.
Review of modern computational optimal transport methods for biomedical applications.
Paper relaxes optimal transport using convex functions for data science.
Study optimal transport on simplex boundary, proving transport map and potential regularity.
Bayesian approach to optimal transport with stochastic costs.
TSC uses HMC and adaptive transport maps to optimize forward KL for variational inference.
In this paper, we present a novel and principled approach to learn the optimal transport between two distributions, from samples. Guided by the optimal transport theory, we learn the optimal Kantorovich potential which induces the optimal transport map. This involves learning two convex functions, by solving a novel mi…
New method uses kernel Stein discrepancy for measure transport without strict continuity constraints.
A new model corrects inhomogeneity in Optimal Transport with Boundary.
Paper investigates optimal transport map estimation in infinite-dimensional spaces.
Survey of linking information geometry and optimal transport.
New optimal transport method handles mass creation and destruction.
New method designs fairer transport plans with uncertainty.