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Found 20 papers, 5 papers with code
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models
To quickly assess the spatiotemporal variations of groundwater contamination under uncertain climate disturbances, we developed a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Operator (U-FNO) to solve Partial Differential Equations (PDEs) of groundwater flow and transport simulations at the site scale. We develop a combined loss function that includes both data-driven factors and physical boundary constraints at multiple spatiotemporal scales.
Physical Computing for Materials Acceleration Platforms
A ''technology lottery'' describes a research idea or technology succeeding over others because it is suited to the available software and hardware, not necessarily because it is superior to alternative directions--examples abound, from the synergies of deep learning and GPUs to the disconnect of urban design and autonomous vehicles.
The Unreasonable Effectiveness of Deep Evidential Regression
There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains.
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.
Multivariate Deep Evidential Regression
We discuss three issues with a proposed solution to extract aleatoric and epistemic uncertainties from regression-based neural networks.
Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization
Our work aims to enable more visual communication of large-scale climate impacts via visualizing the output of coastal flood models as satellite imagery.
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.
Learnings from Frontier Development Lab and SpaceML -- AI Accelerators for NASA and ESA
Research with AI and ML technologies lives in a variety of settings with often asynchronous goals and timelines: academic labs and government organizations pursue open-ended research focusing on discoveries with long-term value, while research in industry is driven by commercial pursuits and hence focuses on short-term timelines and return on investment.
Physics-informed GANs for Coastal Flood Visualization
As climate change increases the intensity of natural disasters, society needs better tools for adaptation.
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels
We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases.
Technology Readiness Levels for AI & ML
Drawing on experience in both spacecraft engineering and AI/ML (from research through product), we propose a proven systems engineering approach for machine learning development and deployment.
Manifolds for Unsupervised Visual Anomaly Detection
Through theoretical and empirical explorations of manifold shapes, we develop a novel hyperspherical Variational Auto-Encoder (VAE) via stereographic projections with a gyroplane layer - a complete equivalent to the Poincar\'e VAE.
Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priors
Prototypical networks have been shown to perform well at few-shot learning tasks in computer vision.
Doubly Bayesian Optimization
Probabilistic programming systems enable users to encode model structure and naturally reason about uncertainties, which can be leveraged towards improved Bayesian optimization (BO) methods.
Cortical Microcircuits from a Generative Vision Model
Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence.
Clustering Time-Series Energy Data from Smart Meters
Investigations have been performed into using clustering methods in data mining time-series data from smart meters.
A Pareto Optimal D* Search Algorithm for Multiobjective Path Planning
The global path plan can be calculated with a variety of informed search algorithms, most notably the A* search method, guaranteed to deliver a complete and optimal solution that minimizes the path cost.
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data.
Ranked #4 on
Anomaly Detection
on Numenta Anomaly Benchmark
A Pareto Front-Based Multiobjective Path Planning Algorithm
Path planning is one of the most vital elements of mobile robotics.
