203,800 research outputs found
Learning Controllable Adaptive Simulation for Multi-resolution Physics
Simulating the time evolution of physical systems is pivotal in many
scientific and engineering problems. An open challenge in simulating such
systems is their multi-resolution dynamics: a small fraction of the system is
extremely dynamic, and requires very fine-grained resolution, while a majority
of the system is changing slowly and can be modeled by coarser spatial scales.
Typical learning-based surrogate models use a uniform spatial scale, which
needs to resolve to the finest required scale and can waste a huge compute to
achieve required accuracy. In this work, we introduce Learning controllable
Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep
learning-based surrogate model that jointly learns the evolution model and
optimizes appropriate spatial resolutions that devote more compute to the
highly dynamic regions. LAMP consists of a Graph Neural Network (GNN) for
learning the forward evolution, and a GNN-based actor-critic for learning the
policy of spatial refinement and coarsening. We introduce learning techniques
that optimizes LAMP with weighted sum of error and computational cost as
objective, allowing LAMP to adapt to varying relative importance of error vs.
computation tradeoff at inference time. We evaluate our method in a 1D
benchmark of nonlinear PDEs and a challenging 2D mesh-based simulation. We
demonstrate that our LAMP outperforms state-of-the-art deep learning surrogate
models, and can adaptively trade-off computation to improve long-term
prediction error: it achieves an average of 33.7% error reduction for 1D
nonlinear PDEs, and outperforms MeshGraphNets + classical Adaptive Mesh
Refinement (AMR) in 2D mesh-based simulations. Project website with data and
code can be found at: http://snap.stanford.edu/lamp.Comment: ICLR 2023, notable top-25% (spotlight), 19 pages, 9 figure
Adaptive Optics for Astronomy
Adaptive Optics is a prime example of how progress in observational astronomy
can be driven by technological developments. At many observatories it is now
considered to be part of a standard instrumentation suite, enabling
ground-based telescopes to reach the diffraction limit and thus providing
spatial resolution superior to that achievable from space with current or
planned satellites. In this review we consider adaptive optics from the
astrophysical perspective. We show that adaptive optics has led to important
advances in our understanding of a multitude of astrophysical processes, and
describe how the requirements from science applications are now driving the
development of the next generation of novel adaptive optics techniques.Comment: to appear in ARA&A vol 50, 201
Stellar photometry with Multi Conjugate Adaptive Optics
We overview the current status of photometric analyses of images collected
with Multi Conjugate Adaptive Optics (MCAO) at 8-10m class telescopes that
operated, or are operating, on sky. Particular attention will be payed to
resolved stellar population studies. Stars in crowded stellar systems, such as
globular clusters or in nearby galaxies, are ideal test particles to test AO
performance. We will focus the discussion on photometric precision and accuracy
reached nowadays. We briefly describe our project on stellar photometry and
astrometry of Galactic globular clusters using images taken with GeMS at the
Gemini South telescope. We also present the photometry performed with DAOPHOT
suite of programs into the crowded regions of these globulars reaching very
faint limiting magnitudes Ks ~21.5 mag on moderately large fields of view (~1.5
arcmin squared). We highlight the need for new algorithms to improve the
modeling of the complex variation of the Point Spread Function across the field
of view. Finally, we outline the role that large samples of stellar standards
plays in providing a detailed description of the MCAO performance and in
precise and accurate colour{magnitude diagrams.Comment: 17 pages, 12 figures, SPIE 201
MAESTRO: An Adaptive Low Mach Number Hydrodynamics Algorithm for Stellar Flows
Many astrophysical phenomena are highly subsonic, requiring specialized
numerical methods suitable for long-time integration. In a series of earlier
papers we described the development of MAESTRO, a low Mach number stellar
hydrodynamics code that can be used to simulate long-time, low-speed flows that
would be prohibitively expensive to model using traditional compressible codes.
MAESTRO is based on an equation set derived using low Mach number asymptotics;
this equation set does not explicitly track acoustic waves and thus allows a
significant increase in the time step. MAESTRO is suitable for two- and
three-dimensional local atmospheric flows as well as three-dimensional
full-star flows. Here, we continue the development of MAESTRO by incorporating
adaptive mesh refinement (AMR). The primary difference between MAESTRO and
other structured grid AMR approaches for incompressible and low Mach number
flows is the presence of the time-dependent base state, whose evolution is
coupled to the evolution of the full solution. We also describe how to
incorporate the expansion of the base state for full-star flows, which involves
a novel mapping technique between the one-dimensional base state and the
Cartesian grid, as well as a number of overall improvements to the algorithm.
We examine the efficiency and accuracy of our adaptive code, and demonstrate
that it is suitable for further study of our initial scientific application,
the convective phase of Type Ia supernovae.Comment: Accepted to Astrophysical Journal Suppliment (http://iop.org). 56
pages, 15 figures
Prospects for large-scale financial systems simulation
As the 21st century unfolds, we find ourselves having to control, support, manage or otherwise cope with large-scale complex adaptive systems to an extent that is unprecedented in human history. Whether we are concerned with issues of food security, infrastructural resilience, climate change, health care, web science, security, or financial stability, we face problems that combine scale, connectivity, adaptive dynamics, and criticality. Complex systems simulation is emerging as the key scientific tool for dealing with such complex adaptive systems. Although a relatively new paradigm, it is one that has already established a track record in fields as varied as ecology (Grimm and Railsback, 2005), transport (Nagel et al., 1999), neuroscience (Markram, 2006), and ICT (Bullock and Cliff, 2004). In this report, we consider the application of simulation methodologies to financial systems, assessing the prospects for continued progress in this line of research
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