Engineering topics

Silva, Sam

Publications and source records attributed to Silva, Sam.

6 records

DOE OSTI2024

Two-Step Hyperparameter Optimization Method: Accelerating Hyperparameter Search by Using a Fraction of a Training Dataset

Abstract Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic—primarily relying on manual or grid searches. This is partly because adopting advanced HPO algorithms introduces added complexity to the workflow, leading to longer computation times. This poses a notable challenge to ML applications, as suboptimal hyperparameter selections curtail the potential of ML model performance, ultimately obstructing the full exploitation of ML techniques. In this article, we present a two-step HPO method as a strategic solution to curbing computational demands and wait times, gleaned from practical experiences in applied ML parameterization work. The initial phase involves a preliminary evaluation of hyperparameters on a small subset of the training dataset, followed by a reevaluation of the top-performing candidate models postretraining with the entire training dataset. This two-step HPO method is universally applicable across HPO search algorithms, and we argue it has attractive efficiency gains. As a case study, we present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation. Although our primary use case is a data-rich limit with many millions of samples, we also find that using up to 0.0025% of the data—a few thousand samples—in the initial step is sufficient to find optimal hyperparameter configurations from much more extensive sampling, achieving up to 135× speedup. The benefits of this method materialize through an assessment of hyperparameters and model performance, revealing the minimal model complexity required to achieve the best performance. The assortment of top-performing models harvested from the HPO process allows us to choose a high-performing model with a low inference cost for efficient use in global climate models (GCMs).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Technical note: AQMEII4 Activity 1: evaluation of wet and dry deposition schemes as an integral part of regional-scale air quality models

We present in this technical note the research protocol for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided into two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reduction of input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (to be described in future submissions as part of the special issue on AQMEII4). The scope of this paper is to present the scientific protocols for Activity 1, as well as to summarize the technical information associated with the different dry deposition approaches used by the participating research groups of AQMEII4. In addition to describing all common aspects and data used for this multi-model evaluation activity, most importantly, we present the strategy devised to allow a common process-level comparison of dry deposition obtained from models using sometimes very different dry deposition schemes. The strategy is based on adding detailed diagnostics to the algorithms used in the dry deposition modules of existing regional air quality models, in particular archiving diagnostics specific to land use–land cover (LULC) and creating standardized LULC categories to facilitate cross-comparison of LULC-specific dry deposition parameters and processes, as well as archiving effective conductance and effective flux as means for comparing the relative influence of different pathways towards the net or total dry deposition. This new approach, along with an analysis of precipitation and wet deposition fields, will provide an unprecedented process-oriented comparison of deposition in regional air quality models. Examples of how specific dry deposition schemes used in participating models have been reduced to the common set of comparable diagnostics defined for AQMEII4 are also presented.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Deep Learning for Ensemble Forecasting

Focal Area: (2) Predictive modeling through the use of AI techniques and AI-derived model components and the use of AI and other tools to design a prediction system comprising a hierarchy of models. Science Challenge: While both climate and weather forecast systems have continued to improve due to substantial efforts to improve computational capabilities, observations, and numerical models, the atmosphere is a chaotic system, and this puts a fundamental limit on our ability to make predictions. Forecasts made by high-resolution models initialized with only slightly different atmospheric states can quickly diverge. Quantifying uncertainty in forecasts is essential to adequately understand them and to make the best-informed policy decisions particularly when it comes to hydrology, extreme weather (including extreme precipitation events), and climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Facilitating better and faster simulations of aerosol-cloud interactions in Earth system models

Focal Area(s): 1. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising a hierarchy of models. 2. Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: One major challenge that Earth system models (ESMs) face in providing credible prediction of the Earth system and its water cycle characteristics (e.g., mean state, variability, and extreme events) is to accurately simulate aerosol-cloud interactions (ACI). The physical, chemical, and dynamical processes affecting ACI are extremely complex and they range from nanoscale to planetary scale. In each model development cycle, scientists spend significant efforts investigating model deficiencies and uncertainties associated with aerosols (e.g., emissions, chemical processes, aerosol microphysics, and transport) and clouds (e.g., macrophysics, microphysics, turbulence, and large-scale circulation) in order to develop improved treatments. However, despite decades of active research, ACI is still a major source of uncertainty in climate projections, even though great progress has been made. Specific scientific challenges include: (i) Parameterizations are developed based on limited data; (ii) The complexity of a parameterization required for accurate predictions is not understood; (iii) Incomplete and unknown physics leads to errors in the fully coupled Earth system; and (iv) Complex physics is computationally too expensive to employ in ESMs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

The Usage of Observing System Simulation Experiments and Reinforcement Learning to Optimize Experimental Design and Operation

Various organizations regularly conduct field campaigns across the globe designed to probe and improve atmospheric process understanding. However, these campaigns are mostly designed ad-hoc, and rely on anecdotes and knowledge of what was successful in previous campaigns. Such ad-hoc experiment design results in sub-optimal instrument siting and operation strategies. Inadequate targeted data collection of extreme weather is a major limitation to Earth and Environmental Systems Science Division (EESSD)’s goals of data model integration (4.5.3), which limits the knowledge gained through the collected observations, holding back significant advances in predictability. While advances in instrument design are always chipping away at these limitations, we are not optimally utilizing the instrumentation we currently possess.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

OpenSearch Status

We will present an overview of our OpenSearch efforts over the past 6 months. We will discuss our Best Practices and those of CEOS concentrating on the compatibility issues between the two. We will also discuss the state of earth data OpenSearch implementations and their adherence to the standards, extensions and best practices available.

OpenSearch↗