Related papers: Learning Post-Newtonian Corrections from Numerical…
Dissipative tidal interactions can be used to probe the out-of-equilibrium physics of neutron stars using gravitational wave observations. In this paper, we present the first post-Newtonian (PN) corrections to the orbital dynamics of a…
We study the astrophysical impact of inaccurate and incomplete modeling of the gravitational waveforms from compact binary coalescences (CBCs). We do so by the matched filtering of complete inspiral-merger-ringdown (IMR) signals with a bank…
We analyse an eleven-orbit inspiral of a non-spinning black-hole binary with mass ratio q=M1/M2=4. The numerically obtained gravitational waveforms are compared with post-Newtonian (PN) predictions including several sub-dominant multipoles…
Contraction analysis offers, through elegant mathematical developments, a unified way of designing observers for a general class of nonlinear systems, where the observer correction term is obtained by solving an infinite dimensional…
Gravitational wave (GW) astronomy has consolidated its role as a new observational window to reveal the properties of compact binaries in the Universe. In particular, the discovery of the first binary neutron star coalescence, GW170817, led…
We present a time domain waveform model that describes the inspiral-merger-ringdown (IMR) of compact binary systems whose components are non-spinning, and which evolve on orbits with low to moderate eccentricity. The inspiral evolution is…
Using the multipolar post-Minkowskian and matching formalism we compute the gravitational waveform of inspiralling compact binaries moving in quasi-circular orbits at the second and a half post-Newtonian (2.5PN) approximation to general…
Analytic approximation methods in general relativity play a very important role when analyzing the gravitational wave signals recently discovered by the LIGO and Virgo detectors. In this contribution, we present the state-of-the-art and…
One way to produce complete inspiral-merger-ringdown gravitational waveforms from black-hole-binary systems is to connect post-Newtonian (PN) and numerical-relativity (NR) results to create "hybrid" waveforms. Hybrid waveforms are central…
The present work investigates the use of physics-informed neural networks (PINNs) for the 3D reconstruction of unsteady gravity currents from limited data. In the PINN context, the flow fields are reconstructed by training a neural network…
Physics-Informed Neural Networks (PINNs) are effective methods for solving inverse problems and discovering governing equations from observational data. However, their performance degrades significantly under complex measurement noise and…
Numerical modeling errors are unavoidable in finite element analysis. The presence of model errors inherently reflects both model accuracy and uncertainty. To date there have been few methods for explicitly quantifying errors at points of…
Physics-informed neural networks (PINNs) are one popular approach to incorporate a priori knowledge about physical systems into the learning framework. PINNs are known to be robust for smaller training sets, derive better generalization…
Physics-informed neural networks (PINNs) have great potential for flexibility and effectiveness in forward modeling and inversion of seismic waves. However, coordinate-based neural networks (NNs) commonly suffer from the "spectral bias"…
Next-generation gravitational-wave detectors are expected to constrain the properties of extreme density matter via observations of static and dynamical tides in binary neutron star inspirals. The required modelling is straightforward in…
We present ${\tt NRPMw}$, an analytical model of gravitational-waves from neutron star merger remnants informed using 618 numerical relativity (NR) simulations. ${\tt NRPMw}$ is designed in the frequency domain using a combination of…
Physics-informed neural networks (PINNs) offer a unified framework for solving both forward and inverse problems of differential equations, yet their performance and physical consistency strongly depend on how governing laws are…
Our recent intensive study has found that physics-informed neural networks (PINN) tend to be local approximators after training. This observation leads to this novel physics-informed radial basis network (PIRBN), which can maintain the…
We probe the ability of various types of post-Newtonian(PN)-accurate circular templates to capture inspiral gravitational-wave (GW) signals from compact binaries having tiny orbital eccentricities. The GW signals are constructed by adapting…
Physics Informed Neural Networks (PINNs) are shown to be a promising method for the approximation of Partial Differential Equations (PDEs). PINNs approximate the PDE solution by minimizing physics-based loss functions over a given domain.…