Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks | Paper Citation Record

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Spacetime Embedding 8 D

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This paper cites Results 16 A.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Results 16 A

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Observation f5b9f053-3e18-4880-b0d7-a7064a09d8e3 · outbound

This paper cites A classifier of the type may be defined through the complex speciality index ([40, 41]).

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks A classifier of the type may be defined through the complex speciality index ([40, 41])

Reference 8

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This paper cites The relevant infinitesimal sym- metries are the three rotations of the round two-sphere.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks The relevant infinitesimal sym- metries are the three rotations of the round two-sphere

Reference 9

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Observation c611576b-cb62-4bd3-81d9-1b2612400fb4 · outbound

This paper cites The base network still outputs the ambient Cholesky vector, while a wrapper applies Equation (32) and returns the flattened intrinsic metric.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks The base network still outputs the ambient Cholesky vector, while a wrapper applies Equation (32) and returns the flattened intrinsic metric

Reference 10

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This paper cites The network described in Section IIIA is then trained with thisLE,Local loss only, forλ∈ {+1,0,−1}.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks The network described in Section IIIA is then trained with thisLE,Local loss only, forλ∈ {+1,0,−1}

Reference 11

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Observation c390b18a-0b3e-4e40-9ffc-7c752afddf1a · outbound

This paper cites The default Schwarzschild Lorentzian Einstein loss is then defined as 5 By this we meangE is obtained by the same vielbein asg, but usingδas the flat metric, instead ofη.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks The default Schwarzschild Lorentzian Einstein loss is then defined as 5 By this we meangE is obtained by the same vielbein asg, but usingδas the flat metric, instead ofη

Reference 12

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This paper cites A complementary search can instead use the speciality index in the loss function as a repeller from the Schwarzschild/type-DvalueS= 1.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks A complementary search can instead use the speciality index in the loss function as a repeller from the Schwarzschild/type-DvalueS= 1

Reference 13

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This paper cites MathematicsandMachineLearningProgram.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks MathematicsandMachineLearningProgram

Reference 14

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This paper cites The Dynamics of General Relativity.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks The Dynamics of General Relativity

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Initial Data for Numerical Relativity

Reference 16

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Numerical Relativity: A review

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This paper cites Shibata and T.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Shibata and T

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This paper cites Evolution of Binary Black Hole Spacetimes.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Evolution of Binary Black Hole Spacetimes

Reference 20

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This paper cites Accurate Evolutions of Orbiting Black-Hole Binaries Without Excision.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Accurate Evolutions of Orbiting Black-Hole Binaries Without Excision

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Observation ff25c695-d07e-477f-89fa-a1c56615fb72 · outbound

This paper cites Gravitational wave extraction from an inspiraling configuration of merging black holes.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Gravitational wave extraction from an inspiraling configuration of merging black holes

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Observation a8e5e166-ab20-4a6f-8867-a17e6c38af91 · outbound

This paper cites Black-hole binaries, gravitational waves, and numerical relativity.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Black-hole binaries, gravitational waves, and numerical relativity

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This paper cites High-accuracy waveforms for binary black hole inspiral, merger, and ringdown.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks High-accuracy waveforms for binary black hole inspiral, merger, and ringdown

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks The Einstein Toolkit: A Community Computational Infrastructure for Relativistic Astrophysics

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This paper cites Static Axisymmetric Vacuum Solutions and Non-Uniform Black Strings.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Static Axisymmetric Vacuum Solutions and Non-Uniform Black Strings

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This paper cites A new approach to static numerical relativity, and its application to Kaluza-Klein black holes.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks A new approach to static numerical relativity, and its application to Kaluza-Klein black holes

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This paper cites A numerical approach to finding general stationary vacuum black holes.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks A numerical approach to finding general stationary vacuum black holes

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Numerical Methods for Finding Stationary Gravitational Solutions

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This paper cites Lumpy AdS$\bf{_5\times}$ S$\bf{^5}$ Black Holes and Black Belts.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Lumpy AdS$\bf{_5\times}$ S$\bf{^5}$ Black Holes and Black Belts

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This paper cites Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Hirst, T

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks A Machine Learning Approach to the Nirenberg Problem,

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Machine learning Calabi-Yau metrics

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Douglas, S

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Learning Size and Shape of Calabi-Yau Spaces

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks CYJAX: A package for Calabi-Yau metrics with JAX

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Machine Learned Calabi-Yau Metrics and Curvature

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Harmonic $1$-forms on real loci of Calabi-Yau manifolds

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Neural and numerical methods for G2-structures on contact Calabi-Yau 7-manifolds,

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Machine Learning Gravity Compactifications on Negatively Curved Manifolds

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Minimal surfaces, Knots, and Neural Networks

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Black holes and the loss landscape in machine learning

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This paper cites Calculating Quasi-Normal Modes of Schwarzschild Black Holes with Physics Informed Neural Networks.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Calculating Quasi-Normal Modes of Schwarzschild Black Holes with Physics Informed Neural Networks

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Einstein Fields: A Neural Per- spective To Computational General Relativity,

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Machine-Learning the Classification of Spacetimes

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Learning holographic horizons

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Machine learning automorphic forms for black holes

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Hashimoto, K

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Minimising Willmore Energy via Neural Flow

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks PINNs in More General Geometry

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Observation 58aa7340-0319-4cb5-b790-bf0819dd627e · outbound

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Penrose, Physical Review Letters10, 66 (1963)

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks The construction and application of penrose diagrams, with a focus on the maxi- mally analytically extended schwarzschild spacetime,

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Making use of geometrical invariants in black hole collisions

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Stephani, D

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Second Order Scalar Invariants of the Riemann Tensor: Applications to Black Hole Spacetimes

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Rosato, H

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Zakhary and C

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Unresolved cited work

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Unresolved cited work

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Unresolved cited work

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Observation d1251d51-4ed7-4cfc-a9c5-d312d944fe15 · outbound

This paper cites Israel, Physical Review164, 1776 (1967).

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Israel, Physical Review164, 1776 (1967)

Reference 62

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Observation 4f98988a-be51-425f-8bcd-93ca3f2c18c2 · outbound

This paper cites Penrose, Phys.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Penrose, Phys

Reference 63

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Observation 613f68aa-0d61-45f4-a50d-d7c4ab21c35e · outbound

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Unresolved cited work

Reference 64

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Observation 24842574-ff5e-4d0b-9807-875dce467c25 · outbound

This paper cites Isolated and dynamical horizons and their applications.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Isolated and dynamical horizons and their applications

Reference 65

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Observation 7b63d075-9b78-46b1-81db-59cb0d448b79 · outbound

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Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Unresolved cited work

Reference 66

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Observation 656db1b2-fde2-4923-977e-3fe5cbfa263f · outbound

This paper cites Quadrupolar gravitational fields described by the $q-$metric.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Quadrupolar gravitational fields described by the $q-$metric

Reference 67

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Observation 3a360433-c11f-4220-b62b-d1f4df87bcd3 · outbound

This paper cites Newman and R.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Newman and R

Reference 68

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