Engineering topics

Melchior, Peter

Publications and source records attributed to Melchior, Peter.

3 records

DOE OSTI2024

Simulation-based inference for parameter estimation of complex watershed simulators

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Spatio–Temporal Machine Learning for Regional to Continental Scale Terrestrial Hydrology

Integrated hydrologic models can simulate coupled surface and subsurface processes but are computationally expensive to run at high resolutions over large domains. Here we develop a novel deep learning model to emulate subsurface flows simulated by the integrated ParFlow–CLM model across the contiguous US. We compare convolutional neural networks like ResNet and UNet run autoregressively against our novel architecture called the Forced SpatioTemporal RNN (FSTR). The FSTR model incorporates separate encoding of initial conditions, static parameters, and meteorological forcings, which are fused in a recurrent loop to produce spatiotemporal predictions of groundwater. We evaluate the model architectures on their ability to reproduce 4D pressure heads, water table depths, and surface soil moisture over the contiguous US at 1 km resolution and daily time steps over the course of a full water year. The FSTR model shows superior performance to the baseline models, producing stable simulations that capture both seasonal and event–scale dynamics across a wide array of hydroclimatic regimes. The emulators provide over 1,000× speedup compared to the original physical model, which will enable new capabilities like uncertainty quantification and data assimilation for integrated hydrologic modeling that were not previously possible. Our results demonstrate the promise of using specialized deep learning architectures like FSTR for emulating complex process–based models without sacrificing fidelity.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Three Gravitationally Lensed Supernovae Behind Clash Galaxy Clusters

We report observations of three gravitationally lensed supernovae (SNe) in the Cluster Lensing And Supernova survey with Hubble (CLASH) Multi-Cycle Treasury program. These objects, SN CLO12Car (z = 1.28), SN CLN12Did (z = 0.85), and SN CLA11Tib (z = 1.14), are located behind three different clusters, MACSJ1720.2+3536 (z = 0.391), RXJ1532.9+3021 (z = 0.345), and A383 (z = 0.187), respectively. Each SN was detected in Hubble Space Telescope optical and infrared images. Based on photometric classification, we find that SNe CLO12Car and CLN12Did are likely to be Type Ia supernovae (SNe Ia), while the classification of SN CLA11Tib is inconclusive. Using multi-color light-curve fits to determine a standardized SN Ia luminosity distance, we infer that SN CLO12Car was approx. 1.0 +/- 0.2 mag brighter than field SNe Ia at a similar redshift and ascribe this to gravitational lens magnification. Similarly, SN CLN12Did is approx. 0.2 +/- 0.2 mag brighter than field SNe Ia. We derive independent estimates of the predicted magnification from CLASH strong+weak-lensing maps of the clusters (in magnitude units, 2.5 log10 μ): 0.83 +/- 0.16 mag for SN CLO12Car, 0.28 +/- 0.08 mag for SN CLN12Did, and 0.43 +/- 0.11 mag for SN CLA11Tib. The two SNe Ia provide a new test of the cluster lens model predictions: we find that the magnifications based on the SN Ia brightness and those predicted by the lens maps are consistent. Our results herald the promise of future observations of samples of cluster-lensed SNe Ia (from the ground or space) to help illuminate the dark-matter distribution in clusters of galaxies, through the direct determination of absolute magnifications.

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