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Found 31 papers, 14 papers with code
Closing the Gap Between SGP4 and High-Precision Propagation via Differentiable Programming
Thus, we propose a novel orbital propagation paradigm, ML-dSGP4, where neural networks are integrated into the orbital propagator.
High-Cadence Thermospheric Density Estimation enabled by Machine Learning on Solar Imagery
Accurate estimation of thermospheric density is critical for precise modeling of satellite drag forces in low Earth orbit (LEO).
Managing AI Risks in an Era of Rapid Progress
In this short consensus paper, we outline risks from upcoming, advanced AI systems.
Gradients without Backpropagation
Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning.
Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles
Solar radio flux along with geomagnetic indices are important indicators of solar activity and its effects.
Simulation Intelligence: Towards a New Generation of Scientific Methods
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
Detecting and Quantifying Malicious Activity with Simulation-based Inference
We propose the use of probabilistic programming techniques to tackle the malicious user identification problem in a recommendation algorithm.
KL Guided Domain Adaptation
A common approach in the domain adaptation literature is to learn a representation of the input that has the same (marginal) distribution over the source and the target domain.
Domain Invariant Representation Learning with Domain Density Transformations
Domain generalization refers to the problem where we aim to train a model on data from a set of source domains so that the model can generalize to unseen target domains.
Technology Readiness Levels for Machine Learning Systems
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning
Our approach establishes the framework for a novel technique to calibrate EUV instruments and advance our understanding of the cross-channel relation between different EUV channels.
Towards Automated Satellite Conjunction Management with Bayesian Deep Learning
After decades of space travel, low Earth orbit is a junkyard of discarded rocket bodies, dead satellites, and millions of pieces of debris from collisions and explosions.
Spacecraft Collision Risk Assessment with Probabilistic Programming
Over 34, 000 objects bigger than 10 cm in length are known to orbit Earth.
AutoSimulate: (Quickly) Learning Synthetic Data Generation
Simulation is increasingly being used for generating large labelled datasets in many machine learning problems.
Simulation-Based Inference for Global Health Decisions
The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies.
Black-Box Optimization with Local Generative Surrogates
To address such cases, we introduce the use of deep generative models to iteratively approximate the simulator in local neighborhoods of the parameter space.
Transflow Learning: Repurposing Flow Models Without Retraining
It is well known that deep generative models have a rich latent space, and that it is possible to smoothly manipulate their outputs by traversing this latent space.
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties
Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient.
Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses
Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation.
Probabilistic Surrogate Networks for Simulators with Unbounded Randomness
Our surrogates target stochastic simulators where the number of random variables itself can be stochastic and potentially unbounded.
Attention for Inference Compilation
We present a new approach to automatic amortized inference in universal probabilistic programs which improves performance compared to current methods.
Amortized Rejection Sampling in Universal Probabilistic Programming
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance.
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo
We introduce a recent symplectic integration scheme derived for solving physically motivated systems with non-separable Hamiltonians.
Flood Detection On Low Cost Orbital Hardware
Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding.
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.
Hijacking Malaria Simulators with Probabilistic Programming
Epidemiology simulations have become a fundamental tool in the fight against the epidemics of various infectious diseases like AIDS and malaria.
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
We expand upon their approach by presenting a new machine learning model, \texttt{plan-net}, based on an ensemble of Bayesian neural networks that yields more accurate inferences than the random forest for the same data set of synthetic transmission spectra.
Alpha MAML: Adaptive Model-Agnostic Meta-Learning
Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.
Tricks from Deep Learning
The deep learning community has devised a diverse set of methods to make gradient optimization, using large datasets, of large and highly complex models with deeply cascaded nonlinearities, practical.
DiffSharp: An AD Library for .NET Languages
DiffSharp is an algorithmic differentiation or automatic differentiation (AD) library for the . NET ecosystem, which is targeted by the C# and F# languages, among others.
