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Willett, Rebecca

Publications and source records attributed to Willett, Rebecca.

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Chromatin structures from integrated AI and polymer physics model

The physical organization of the genome in three-dimensional space regulates many biological processes, including gene expression and cell differentiation. Three-dimensional characterization of genome structure is critical to understanding these biological processes. Direct experimental measurements of genome structure are challenging; computational models of chromatin structure are therefore necessary. We develop an approach that combines a particle-based chromatin polymer model, molecular simulation, and machine learning to efficiently and accurately estimate chromatin structure fromindirectmeasures of genome structure. More specifically, we introduce a new approach where the interaction parameters of the polymer model are extracted from experimental Hi-C data using a graph neural network (GNN). We train the GNN on simulated data from the underlying polymer model, avoiding the need for large quantities of experimental data. The resulting approach accurately estimates chromatin structures across all chromosomes and across several experimental cell lines despite being trained almost exclusively on simulated data. The proposed approach can be viewed as a general framework for combining physical modeling with machine learning, and it could be extended to integrate additional biological data modalities. Ultimately, we achieve accurate and high-throughput estimations of chromatin structure from Hi-C data, which will be necessary as experimental methodologies, such as single-cell Hi-C, improve.

Biochemistry & Molecular Biology↗
DOE OSTI2023

Reduced-order autodifferentiable ensemble Kalman filters

This paper introduces a computational framework to reconstruct and forecast a partially observed state that evolves according to an unknown or expensive-to-simulate dynamical system. Our reduced-order autodifferentiable ensemble Kalman filters (ROAD-EnKFs) learn a latent low-dimensional surrogate model for the dynamics and a decoder that maps from the latent space to the state space. The learned dynamics and decoder are then used within an EnKF to reconstruct and forecast the state. Numerical experiments show that if the state dynamics exhibit a hidden low-dimensional structure, ROAD-EnKFs achieve higher accuracy at lower computational cost compared to existing methods. If such structure is not expressed in the latent state dynamics, ROAD-EnKFs achieve similar accuracy at lower cost, making them a promising approach for surrogate state reconstruction and forecasting.

Mathematics↗
DOE OSTI2023

Climate-driven changes in the predictability of seasonal precipitation

Climate-driven changes in precipitation amounts and their seasonal variability are expected in many continental-scale regions during the remainder of the 21st century. However, much less is known about future changes in the predictability of seasonal precipitation, an important earth system property relevant for climate adaptation. Here, on the basis of CMIP6 models that capture the present-day teleconnections between seasonal precipitation and previous-season sea surface temperature (SST), we show that climate change is expected to alter the SST-precipitation relationships and thus our ability to predict seasonal precipitation by 2100. Specifically, in the tropics, seasonal precipitation predictability from SSTs is projected to increase throughout the year, except the northern Amazonia during boreal winter. Concurrently, in the extra-tropics predictability is likely to increase in central Asia during boreal spring and winter. The altered predictability, together with enhanced interannual variability of seasonal precipitation, poses new opportunities and challenges for regional water management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Autodifferentiable Ensemble Kalman Filters

Data assimilation is concerned with sequentially estimating a temporally evolving state. This task, which arises in a wide range of scientific and engineering applications, is particularly challenging when the state is high-dimensional and the state-space dynamics are unknown. This paper introduces a machine learning framework for learning dynamical systems in data assimilation. Here, our auto-differentiable ensemble Kalman filters (AD-EnKFs) blend ensemble Kalman filters for state recovery with machine learning tools for learning the dynamics. In doing so, AD-EnKFs leverage the ability of ensemble Kalman filters to scale to high-dimensional states and the power of automatic differentiation to train high-dimensional surrogate models for the dynamics. Numerical results using the Lorenz-96 model show that AD-EnKFs outperform existing methods that use expectation-maximization or particle filters to merge data assimilation and machine learning. In addition, AD-EnKFs are easy to implement and require minimal tuning.

autodifferentiation↗
NASA NTRS2017

Asymmetric Expansion of the Youngest Galactic Supernova Remnant G1.9+0.3

The youngest Galactic supernova remnant (SNR) G1.9+0.3, produced by a (probable) SN Ia that exploded approximately 1900 CE, is strongly asymmetric at radio wavelengths, much brighter in the north, but bilaterally symmetric in X-rays. We present the results of X-ray expansion measurements that illuminate the origin of the radio asymmetry. We confirm the mean expansion rate (2011-2015) of 0.58% per yr, but large spatial variations are present. Using the nonparametric 'Demons' method, we measure the velocity field throughout the entire SNR, finding that motions vary by a factor of 5, from 0.''09 to 0.''44 per yr. The slowest shocks are at the outer boundary of the bright northern radio rim, with velocities v(sub s) as low as 3600 km per sec (for an assumed distance of 8.5 kpc), much less than v(sub s) = 12,000-13,000 km per sec along the X-ray-bright major axis. Such strong deceleration of the northern blast wave most likely arises from the collision of SN ejecta with a much denser than average ambient medium there. This asymmetric ambient medium naturally explains the radio asymmetry. In several locations, significant morphological changes and strongly nonradial motions are apparent. The spatially integrated X-ray flux continues to increase with time. Based on Chandra observations spanning 8.3 yr, we measure its increase at 1.3% +/- 0.8% per yr. The SN ejecta are likely colliding with the asymmetric circumstellar medium ejected by the SN progenitor prior to its explosion.

ISM: individual objects (G1.9+0.3) ISM: individual↗
NASA NTRS2014

Nonuniform Expansion of the Youngest Galactic Supernova Remnant G1.9+0.3

We report measurements of the X-ray expansion of the youngest Galactic supernova remnant, G1.9+0.3, using Chandra observations in 2007, 2009, and 2011. The measured rates strongly deviate from uniform expansion, decreasing radially by about 60 along the X-ray bright SE-NW axis from 0.84 plus or minus 0.06% yr(exp -1) to 0.52% plus or minus 0.03 yr(exp -1). This corresponds to undecelerated ages of 120-190 yr, confirming the young age of G1.9+0.3 and implying a significant deceleration of the blast wave. The synchrotron-dominated X-ray emission brightens at a rate of 1.9% plus or minus 0.4% yr(exp -1). We identify bright outer and inner rims with the blast wave and reverse shock, respectively. Sharp density gradients in either the ejecta or ambient medium are required to produce the sudden deceleration of the reverse shock or the blast wave implied by the large spread in expansion ages. The blast wave could have been decelerated recently by an encounter with a modest density discontinuity in the ambient medium, such as may be found at a wind termination shock, requiring strong mass loss in the progenitor.

NONUNIFORM EXPANSION↗