173 research outputs found
Physics Informed Neural Networks for Simulating Radiative Transfer
We propose a novel machine learning algorithm for simulating radiative
transfer. Our algorithm is based on physics informed neural networks (PINNs),
which are trained by minimizing the residual of the underlying radiative
tranfer equations. We present extensive experiments and theoretical error
estimates to demonstrate that PINNs provide a very easy to implement, fast,
robust and accurate method for simulating radiative transfer. We also present a
PINN based algorithm for simulating inverse problems for radiative transfer
efficiently
A Multi-level procedure for enhancing accuracy of machine learning algorithms
We propose a multi-level method to increase the accuracy of machine learning
algorithms for approximating observables in scientific computing, particularly
those that arise in systems modeled by differential equations. The algorithm
relies on judiciously combining a large number of computationally cheap
training data on coarse resolutions with a few expensive training samples on
fine grid resolutions. Theoretical arguments for lowering the generalization
error, based on reducing the variance of the underlying maps, are provided and
numerical evidence, indicating significant gains over underlying single-level
machine learning algorithms, are presented. Moreover, we also apply the
multi-level algorithm in the context of forward uncertainty quantification and
observe a considerable speed-up over competing algorithms
Neural Inverse Operators for Solving PDE Inverse Problems
A large class of inverse problems for PDEs are only well-defined as mappings
from operators to functions. Existing operator learning frameworks map
functions to functions and need to be modified to learn inverse maps from data.
We propose a novel architecture termed Neural Inverse Operators (NIOs) to solve
these PDE inverse problems. Motivated by the underlying mathematical structure,
NIO is based on a suitable composition of DeepONets and FNOs to approximate
mappings from operators to functions. A variety of experiments are presented to
demonstrate that NIOs significantly outperform baselines and solve PDE inverse
problems robustly, accurately and are several orders of magnitude faster than
existing direct and PDE-constrained optimization methods
Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning
Recently, operator learning, or learning mappings between
infinite-dimensional function spaces, has garnered significant attention,
notably in relation to learning partial differential equations from data.
Conceptually clear when outlined on paper, neural operators necessitate
discretization in the transition to computer implementations. This step can
compromise their integrity, often causing them to deviate from the underlying
operators. This research offers a fresh take on neural operators with a
framework Representation equivalent Neural Operators (ReNO) designed to address
these issues. At its core is the concept of operator aliasing, which measures
inconsistency between neural operators and their discrete representations. We
explore this for widely-used operator learning techniques. Our findings detail
how aliasing introduces errors when handling different discretizations and
grids and loss of crucial continuous structures. More generally, this framework
not only sheds light on existing challenges but, given its constructive and
broad nature, also potentially offers tools for developing new neural
operators.Comment: 28 page
Gaia's Cepheids and RR Lyrae Stars and Luminosity Calibrations Based on Tycho-Gaia Astrometric Solution
Gaia Data Release 1 contains parallaxes for more than 700 Galactic Cepheids
and RR Lyrae stars, computed as part of the Tycho-Gaia Astrometric Solution
(TGAS). We have used TGAS parallaxes, along with literature () photometry and spectroscopy, to calibrate the zero point
of the Period-Luminosity and Period-Wesenheit relations of classical and type
II Cepheids, and the near-infrared Period-Luminosity,
Period-Luminosity-Metallicity and optical Luminosity-Metallicity relations of
RR Lyrae stars. In this contribution we briefly summarise results obtained by
fitting these basic relations adopting different techniques that operate either
in parallax or distance (absolute magnitude) space.Comment: 5 pages, 4 figures, proceedings for the 22nd Los Alamos Stellar
Pulsation Conference Series Meeting "Wide field variability surveys: a
21st-century perspective", held in San Pedro de Atacama, Chile, Nov. 28 -
Dec. 2, 201
Updated theoretical Period-Age and Period-Age-Color relations for Galactic Classical Cepheids: an application to the Gaia DR2 sample
Updated evolutionary and pulsational model predictions are combined in order
to interpret the properties of Galactic Classical Cepheids in the Gaia Data
Release 2. In particular, the location of the instability strip boundaries and
the analytical relations connecting pulsation periods to the intrinsic stellar
parameters are combined with evolutionary tracks to derive reliable and
accurate period-age, and the first theoretical period-age-color relations in
the Gaia bands for a solar chemical abundance pattern (=, =).
The adopted theoretical framework takes into account possible variations in the
mass-luminosity relation for the core helium-burning stage as due to changes in
the core convective overshooting and/or mass loss efficiency, as well as the
impact on the instability strip boundaries due to different assumptions for
superadiabatic convection efficiency. The inferred period-age and
period-age-color relations are applied to a selected sample of both fundamental
and first overtone Gaia Cepheids, and individual ages for the various adopted
theoretical scenarios are derived. The retrieved age distributions confirm that
a variation in the efficiency of superadiabatic convection in the pulsational
model computations has a negligible effect, whereas a brighter Mass-Luminosity
relation, as produced by mild overshooting, rotation or mass loss, implies
significantly older age predictions. Moreover, older Cepheids are found at
larger Galactocentric distances, while first overtone Cepheids are found to be
systematically older than the fundamental ones. The comparison with independent
age distribution analysis in literature supports the predictive capability of
current theoretical framework.Comment: 14 pages, 11 figures, 8 tables, accepted for publication in MNRA
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