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.
A new AI optimization method uses energy-conserving dynamics inspired by Born-Infeld theory.
In this short Note we would like to bring into the attention of people working in General Relativity a Schwarzschild like metric found by Professor Cleopatra Mociuţchi in sixties. It was obtained by the A. Sommerfeld reasoning from his treatise "Elektrodynamik" but using instead of the energy conserving law from the cl…
Develops ECD framework for optimizing machine learning problems.
Gaussian process model learns Hamiltonian systems from noisy data.
ECD algorithm speeds up non-convex optimization, offering quantum and stochastic enhancements.
Study of energy conservation in fourth-order gravity theories.
LNNs learn Lagrangians without canonical coordinates, conserving energy and relativity.
RNN operators solve Newton's equations with large timesteps for molecular dynamics.
Novel method combines physics priors for energy-conserving dynamics.
In this paper, we study the schrodinger equation and wave equation with the Dirichlet boundary condition on a connected finite graph. The explicit expressions for solutions are given and the energy conservations are derived. Applications to the corresponding nonlinear problems are indicated.
DJIA is tested whether it can be described as a mechanical system conserving total energy with K (=v*v/2) + U, where U is calculated as the negative of work done by force obtained in terms of the second derivative of price, assuming unit mass.
We address the problem of constructing numerical integrators for nonholonomic Lagrangian systems that enjoy appropriate discrete versions of the geometric properties of the continuous flow, including the preservation of energy. Building on previous work on time-dependent discrete mechanics, our approach is based on a d…
New framework models non-conservative stochastic processes without energy conservation constraints.
New approach relaxes inductive biases of physics-inspired NNs for better performance.
One important effect of price shocks in the United States has been increased political attention paid to the structure and performance of oil and natural gas markets, along with some governmental support for energy conservation. This paper describes how price changes helped lead the emergence of a political agenda acco…
A new method, based on the original theory of conservation of sum of kinetic and potential energy defined for prices is proposed and applied on Dow Jones Industrials Average (DJIA). The general trends averaged over months or years gave a roughly conserved total energy, with three different potential energies, i.e. posi…
Many practical machine learning tasks employ very deep convolutional neural networks. Such large depths pose formidable computational challenges in training and operating the network. It is therefore important to understand how fast the energy contained in the propagated signals (a.k.a. feature maps) decays across laye…
Study predicts room occupancy using machine learning, achieving high accuracy.
New approach reduces particle simulation complexity to linear time and space.
The Langevin Markov chain algorithms are widely deployed methods to sample from distributions in challenging high-dimensional and non-convex statistics and machine learning applications. Despite this, current bounds for the Langevin algorithms are slower than those of competing algorithms in many important situations, …
In this paper we study -instantons on asymptotically conical -orbifolds (and manifolds) obtained by filling in certain squashed -Sasakian -manifolds. We construct a -parameter family of explicit -instantons. Taking the parameter to infinity, the family (a) bubbles o…
We consider intersecting hypersurfaces in curved spacetime with gravity governed by a class of actions which are topological invariants in lower dimensionality. Along with the Chern-Simons boundary terms there is a sequence of intersection terms that should be added in the action functional for a well defined variation…
SSINNs learn Hamiltonian systems from data with interpretable, low-memory models.
Neural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates. In many applications physical constraints, such as mass or energy conservation, must be satisfied to obtain reliable results. However, standard machine learning algorithm…
Introduces GFC for learning complex dynamical systems with geometric constraints.
Techniques for reducing the variance of gradient estimates used in stochastic programming algorithms for convex finite-sum problems have received a great deal of attention in recent years. By leveraging dissipativity theory from control, we provide a new perspective on two important variance-reduction algorithms: SVRG …
Proposes ENOs for learning PDE solutions that conserve energy.
Scattering theory for linearised gravity on Schwarzschild black hole exterior.
A new ML method predicts long-time-step molecular dynamics, preserving symplectic and time-reversible properties.
GeoHNN models physics laws for stable, accurate predictions.
Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional posterior distributions with proposed parameter draws obtained by iterating on a discretized version of the Hamiltonian dynamics. The iterations make HMC computationally costly, especially in problems with large datasets, since it is necessary to c…
Unconstrained MLIPs outperform constrained ones in accuracy and speed.
Constrained adaptive filtering algorithms inculding constrained least mean square (CLMS), constrained affine projection (CAP) and constrained recursive least squares (CRLS) have been extensively studied in many applications. Most existing constrained adaptive filtering algorithms are developed under mean square error (…
Researchers found a way to measure energy in black hole perturbations.
The implementation of smart building technology in the form of smart infrastructure applications has great potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. However, human preference in regard to living conditions is usually unknown and heterogeneous in its manifestati…