enabler for sustainable FEW Systems Baskar Ganapathysubramanian Iowa State University NSF FEW Workshop Oct 1213 2015 ISU 1 Computational Science and Engineering Group What do we do Algorithm design and software implementation ID: 682722
Download Presentation The PPT/PDF document "Computational Science as an" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Computational Science as an enablerfor sustainable FEW SystemsBaskar GanapathysubramanianIowa State University
NSF FEW Workshop: Oct 12-13, 2015, ISU
1Slide2
Computational Science and Engineering Group
What do we do:
Algorithm design and software implementation
Application driven research: Curiosity driven group
Overview of research activities related to Plant Sciences
NSF FEW Workshop: Oct 12-13, 2015, ISU
2Slide3
Feature extraction: Data for crop models
Spatial
coverage
(Dimensions
of
field)
Temporal Coverage
(Crop Cycle)
Data for validation/input/calibration
Data deluge due to sensor advances and data collection improvements
Heterogeneous, multi length and time scale data
Noisy,
gappy
data
Need to extract traits used for various ‘down stream’ tasks
Have to do this in an automated, high throughput, and efficient way
Similar issues faced by other disciplines: Astronomy, Particle physics, Driverless automobiles, security and defense applications
Machine learning approaches very promising
NSF FEW Workshop: Oct 12-13, 2015, ISU
3Slide4
Machine LearningGoal of ML is to generalize beyond training dataPattern recognition, perception and control tasksVery difficult to manually encode all features
From opsrules.com
MNIST dataset
TIMIT dataset
Breakthrough in learning algorithms. Prominent examples include ‘deep networks’
NVIDIA
cuDNN
website
More data, Better computing infrastructure
NSF FEW Workshop: Oct 12-13, 2015, ISU
4Slide5
Learning feature labels in scenes: Convolution networks
From Le Cun
group, Hinton group, Ng group
Machine
Learning Examples
NSF FEW Workshop: Oct 12-13, 2015, ISU
5Slide6
From Le Cun group, Hinton group, Ng group
Machine
Learning Examples
Learning a hierarchy of features: Feature extractions using auto-encoders, sparse
encoders, Deep Belief networks, Deep Neural Networks
NSF FEW Workshop: Oct 12-13, 2015, ISU
6Slide7
Basic hypothesis
: Use
high throughput
phenotyping to enable extraction of detailed characteristics of tassels.
Challenges: Identification of tassel locations, followed by extraction of tassel features of close to a million images!
ML: Agricultural Examples
P.
SchnableSlide8
Basic hypothesis
: Use
high throughput
phenotyping to
understand features affecting (a)biotic stress tolerance
A. Singh
A.
Singh
1
2
3
4
5
Standard Area Diagram
Example Application:
Iron Deficiency
Chrolosis
(IDC)
IDC: Inability of plants to absorb iron from soil
Current
Methods are Visual:
Time consuming
Labor Intensive
Reliability/Consistency issues
ML tools for rapid identification. Deploy as apps
ML: Agricultural Examples
S. SarkarSlide9
ML for Yield Prediction
Goal: 1) Collect and curate dataset of economic, agricultural, meteorological, and crop management traits that is used to make
predictions.
2) Develop and deploy suite of statistical and ML tools on data
3) Create a workflow that will enable the larger community to utilize data and test methods
Yield forecasting: Combination of knowledge-based computer programs (that simulate plant-weather-soil-management interactions) along with soil and environment data and targeted surveys.
D. Hayes
Companies
such as Climate Corp
and other big data firms may now be able
to beat the USDA at
yield forecasting
, leading
to detrimental asymmetric markets.
A
publicly available high quality yield prediction tool
will enable the producers to make informed decisions thereby ensuring a symmetrical market.S. Sarkar
D. Nettleton
NSF FEW Workshop: Oct 12-13, 2015, ISU9Slide10
D.
Attinger
M. Gilbert
Simple physiological model of adult
maize plant.
Validated
in field by Matthew
Gilbert (UC Davis)
Several field-testable traits: stomatal conductance, root, stem, leaf conductance.Input: Hourly weather
data.
Outputs: Water use, Photosynthetic yield
Optimization: Trait identification for productivity
Software engineering
Code optimization
Integrate with parallel optimization framework
Deploy on HPC systemsSlide11
Optimization: Trait identification for productivity
Pareto front with more than 3 million configurations tested. Ran on XSEDE TACC and local HPC
resources (unpublished, 2015).
Explored traits that perform under well irrigated vs drought conditions.
NSF FEW Workshop: Oct 12-13, 2015, ISU
11Slide12
Concluding ObservationsLeverage (rapid) machine learning developments
Learn from progress/best practices in other fields Fast ML models as surrogate models for exploration, uncertainty quantification
Visualization
and data management become
important
Data exchange/sharing/interoperability protocols have to be set.Critical to incorporate software engineering practices into the workflow (code reuse, modularity).
Need sustained support for software development and maintenance
Need to be ready for next generation cyber infrastructureCommunity
based approach?