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motivation.html
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<!DOCTYPE html>
<html>
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<title>Motivation</title>
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<section class="home-content-wrap wf-section page-nav-section-marker" id="Data Discovery">
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<div class="label cc-light">Motivation</div>
<h2 class="heading">Data-driven Discovery</h2>
</div>
<div class="rich-text">
<p class="animated-item pre-anim blur-in-anim">
Machine learning offers tools that are particularly well suited to addressing research challenges in
geosciences. Many fundamental earth system processes such as fluid and solid dynamics, affecting everything from
crustal deformation to climate patterns, are non-linear and not well represented in existing deterministic
models. It is also difficult to measure all components of the systems we are studying (e.g. the base of an ice
sheet or a continental plate boundary), but we often can measure the second order impacts of these physical
systems (e.g. ice sheet surface velocity or GPS uplift rates). ML provides novel approaches for discovering
trends in these observations that may illuminate new understandings of driving physical processes. Traditional
geoscience modeling uses a top-down approach based on our understanding of the physical processes and their
representation in physics-based models, but it is limited by that particular representation. In contrast, ML can
discover patterns and trends in large amounts of data, as it is based on a bottom-up approach in which
algorithms learn relationships between input data and output. ML models are also highly efficient and, in some
cases, more accurate because of their flexibility to accommodate non-linearity and/or non-Gaussianity. Recent
approaches combine physics-based modeling from domain science with ML for building powerful prediction systems
that are performant and easier to interpret.
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ML and domain science education to accelerate ML-based scientific discovery
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As geoscience has entered an era in which both in-situ and remote sensing observations are dense and global,
advances in observational techniques have dramatically increased the spatial and temporal resolution and
therefore, data quality and volume. ML tools can assist handling large volumes of observations, modeling,
analysis, and forecasting of the environment by increasing the speed and accuracy of computations.
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</section>
<section class="home-content-wrap wf-section page-nav-section-marker" id="Classroom Adoption">
<div class="title-group animated-item pre-anim blur-in-anim">
<div class="label cc-light">Motivation</div>
<h2 class="heading">ML Adoption in Classrooms</h2>
</div>
<div class="rich-text animated-item pre-anim blur-in-anim">
<p>
With recent computational hardware innovations such as graphics processing units (GPUs), as well as innovations
in ML methods themselves, ML is positioned to assist geoscientists by exploiting untapped environmental
observations and enhancing Earth system modeling in timely and cost-effective ways. However, reproducible ML
workflows require sophisticated coupling of advanced software, data processing pipelines, visualization tools,
and rapidly evolving Cloud-based CI. Adopting ML curriculum in classrooms has great potential
for advancing ML-based discovery in the geosciences through hands-on teaching and project work.
</p>
</div>
</section>
<section class="home-content-wrap wf-section page-nav-section-marker" id="Community Support">
<div class="title-group animated-item pre-anim blur-in-anim">
<div class="label cc-light">Motivation</div>
<h2 class="heading">ML Support for GeoScience Communities</h2>
</div>
<div class="rich-text">
<p class="animated-item pre-anim blur-in-anim">
We will cultivate the development of discipline-specific ML libraries, workflows and communities of practice
capable of sustaining future growth of ML cybertraining opportunities. To address the general lack of
educational material in ML for geoscientific applications we will assemble a team of geoscience educators to
create a novel ML curriculum. These materials could then be included in several university courses to further
broaden our impact on emerging ML communities. Our GeoSMART implementation plan will guide participants through
training in fundamental open source ML toolkits and data science skills.
</p>
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With these fundamental skills, participants will then progress through interactive events including hackweeks,
project-led and peer-to-peer mentoring activities and incubators. By building tools using open source and
cloud-accessible platforms, and by partnering with colleges and institutions who currently lack computing
resources for ML workflows, we will democratize access to our cybertraining materials and ensure more people can
be included in helping to solve urgent geoscience challenges. There is a clear need for scientists that have
both data science skills and domain knowledge, and the demand for these coupled skills is recognized by both
academic and professional environments.
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