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.
Physics-informed model reduces RBC simulation costs.
Physics-informed neural networks improve surrogate modeling of turbulent Rayleigh-Bénard convection.
LGAC enhances heat transfer in turbulent boundary layers using slot jets.
Most transport theorems---that is, a formula for the rate of change of an integral in which both the integrand and domain of integration depend on time---involve domains that evolve according to a flow map. Such domains are said to be convecting. Here a transport theorem for nonconvecting domains evolving on an embedde…
POSL predicts dynamic convection volumes in hemodiafiltration patients.
AI helps forecasters understand TC convective evolution before intensification.
We consider generalized linear transient convection-diffusion problems for differential forms on bounded domains in . These involve Lie derivatives with respect to a prescribed smooth vector field. We construct both new Eulerian and semi-Lagrangian approaches to the discretization of the Lie derivatives…
DiffObs predicts global precipitation with realistic wave modes and low frequency variations.
LPINNs solve complex PDEs by reformulating PINNs on Lagrangian frame, reducing training complexity.
MeshfreeFlowNet generates high-resolution spatio-temporal solutions from low-resolution inputs.
Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate …
Paper presents a new approach to continuum mechanics using port-Hamiltonian framework.
Artificial neural networks estimate model parameters from observations, reducing model errors.
A new method combines classical and machine learning PDE solvers efficiently.
A phase plot of the oil economy is built using the literature data of world oil production, price, and EROEI (Energy Returned on Energy Invested). An analogy between the oil economy and the Benard convection is proposed; some methods of interpretation and forecast of the system behavior are also shown based on "phase p…
DeepONets improve surrogate modeling for engineering systems.
In recent years, data-driven methods have been developed to learn dynamical systems and partial differential equations (PDE). The goal of such work is discovering unknown physics and the corresponding equations. However, prior to achieving this goal, major challenges remain to be resolved, including learning PDE under …
New method detects TC imagery patterns for rapid intensity change.
We present a unified derivation of covariant time derivatives, which transform as tensors under a time-dependent coordinate change. Such derivatives are essential for formulating physical laws in a frame-independent manner. Three specific derivatives are described: convective, corotational, and directional. The covaria…
Despite the progress within the last decades, weather forecasting is still a challenging and computationally expensive task. Current satellite-based approaches to predict thunderstorms are usually based on the analysis of the observed brightness temperatures in different spectral channels and emit a warning if a critic…
We propose a new high-order alternating direction implicit (ADI) finite difference scheme for the solution of initial-boundary value problems of convection-diffusion type with mixed derivatives and non-constant coefficients, as they arise from stochastic volatility models in option pricing. Our approach combines differ…
Defines observer-invariant time derivatives on moving surfaces.
Polynomial chaos expansions on Grassmannian submanifolds for high-dimensional stochastic systems.
Novel approach ensures stability of compact schemes for variable PDEs.
We study the limiting behaviour of the empirical measure of a system of diffusions interacting through their ranks when the number of diffusions tends to infinity. We prove that the limiting dynamics is given by a McKean-Vlasov evolution equation. Moreover, we show that in a wide range of cases the evolution of the cum…
The paper solves complex swing option pricing equations with numerical methods.
The paper explores how multiway data from PDEs can be accurately tracked using EnKF with specific covariance and precision estimators.
The stability and robustness of compact schemes for parabolic PDEs are analyzed.
This paper deals with pricing of European and American options, when the underlying asset price follows Heston model, via the interior penalty discontinuous Galerkin finite element method (dGFEM). The advantages of dGFEM space discretization with Rannacher smoothing as time integrator with nonsmooth initial and boundar…
Climate projections suffer from uncertain equilibrium climate sensitivity. The reason behind this uncertainty is the resolution of global climate models, which is too coarse to resolve key processes such as clouds and convection. These processes are approximated using heuristics in a process called parameterization. Th…
Simulating complex physical systems often involves solving partial differential equations (PDEs) with some closures due to the presence of multi-scale physics that cannot be fully resolved. Therefore, reliable and accurate closure models for unresolved physics remains an important requirement for many computational phy…
New analysis identifies key factors in wildfire-generated thunderstorms.
Generative thermal design learns optimal shapes using multi-agent reinforcement learning.
Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond the range of test facilities. This paper proposes a data-driven approach, Feature Similarity Measurement FFSM), to establish a technical basis…
New boundary treatment improves accuracy for complex PDEs.
Financial derivatives pricing aims to find the fair value of a financial contract on an underlying asset. Here we consider option pricing in the partial differential equations framework. The contemporary models lead to one-dimensional or multidimensional parabolic problems of the convection-diffusion type and generaliz…
Geometrically reformulates elasticity theory using exterior calculus.
Improved deep dynamics models with symmetries for better accuracy and generalization.
In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss. The resulting forward amortized variational inference is a likelihood-free method as its gradient can be sampled without bias and without requiring any evaluation of eith…
Reliable 4D aircraft trajectory prediction, whether in a real-time setting or for analysis of counterfactuals, is important to the efficiency of the aviation system. Toward this end, we first propose a highly generalizable efficient tree-based matching algorithm to construct image-like feature maps from high-fidelity m…
Paper reduces expensive financial risk simulations through efficient MOR.
Bayesian sOED uses PG reinforcement learning for efficient experiment design.
A model order reduction framework reduces financial risk analysis models efficiently.
The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (), decides which physical terms are necessary and which can be removed (because they are physically n…
GO-OED maximizes predictive information gain on nonlinear QoIs.
New framework discovers PDEs from sparse, noisy data.
Single model learns physics from diverse data.
Improved surrogate model for field-valued QoIs using LF and HF simulations.