arXiv subjects

Laura P. Swiler

Publications and source records attributed to Laura P. Swiler.

5 recordsLinked to original sources

arXiv2025

Conditional pathways-based climate attribution

Attribution of climate impacts to natural and anthropogenic source forcings is essential for understanding and addressing climate effects. While standard methods like optimal fingerprinting have been effective for long-term changes, they often struggle in low signal-to-noise regimes typical of short-term forcings or with climate variables loosely related to the forcing. Single-step approaches fail to leverage additional climate information to enhance attribution certainty. To overcome these limitations, we propose a formal statistical framework that incorporates hypothesized physical pathways linking source forcings to downstream impacts. By establishing relationships based on scalar features and simple forcing response models, we create a series of conditional probabilities that describe the likelihood of the final impact. This method captures both primary and secondary processes by which the downstream impact evolves. Through hypothesis testing in a likelihood ratio framework, we demonstrate improved attribution confidence for source magnitudes in low signal-to-noise scenarios. Using the 1991 eruption of Mt. Pinatubo as a case study, we show that incorporating near-surface temperature and stratospheric radiative flux measurements enhances attribution certainty compared to analyses based solely on temperature, even at seasonal and regional scales. This framework holds promise for improving climate attribution assessments for unknown source magnitudes and low signal-to-noise impacts, where traditional methods may falter. Additionally, the formal inclusion of pathways allows for a deeper exploration of complex, multivariate relationships influencing source attribution.

stat.AP↗

arXiv2024

AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery

Advances in robotic control and sensing have propelled the rise of automated scientific laboratories capable of high-throughput experiments. However, automated scientific laboratories are currently limited by human intuition in their ability to efficiently design and interpret experiments in high-dimensional spaces, throttling scientific discovery. We present AutoSciLab, a machine learning framework for driving autonomous scientific experiments, forming a surrogate researcher purposed for scientific discovery in high-dimensional spaces. AutoSciLab autonomously follows the scientific method in four steps: (i) generating high-dimensional experiments (x \in R^D) using a variational autoencoder (ii) selecting optimal experiments by forming hypotheses using active learning (iii) distilling the experimental results to discover relevant low-dimensional latent variables (z \in R^d, with d << D) with a 'directional autoencoder' and (iv) learning a human interpretable equation connecting the discovered latent variables with a quantity of interest (y = f(z)), using a neural network equation learner. We validate the generalizability of AutoSciLab by rediscovering a) the principles of projectile motion and b) the phase transitions within the spin-states of the Ising model (NP-hard problem). Applying our framework to an open-ended nanophotonics challenge, AutoSciLab uncovers a fundamentally novel method for directing incoherent light emission that surpasses the current state-of-the-art (Iyer et al. 2023b, 2020).

cs.LG↗

arXiv2020

An active learning high-throughput microstructure calibration framework for solving inverse structure-process problems in materials informatics

Determining a process-structure-property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process-property linkage by bypassing the materials structure, or in the context of structure-property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process-structure linkage, which has a great implication in reverse engineering. In this work, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization by exploiting high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.

cs.CE↗

arXiv2018

Spoke-Darts for High-Dimensional Blue-Noise Sampling

Blue noise sampling has proved useful for many graphics applications, but remains underexplored in high-dimensional spaces due to the difficulty of generating distributions and proving properties about them. We present a blue noise sampling method with good quality and performance across different dimensions. The method, spoke-dart sampling, shoots rays from prior samples and selects samples from these rays. It combines the advantages of two major high-dimensional sampling methods: the locality of advancing front with the dimensionality-reduction of hyperplanes, specifically line sampling. We prove that the output sampling is saturated with high probability, with bounds on distances between pairs of samples and between any domain point and its nearest sample. We demonstrate spoke-dart applications for approximate Delaunay graph construction, global optimization, and robotic motion planning. Both the blue-noise quality of the output distribution and the adaptability of the intermediate processes of our method are useful in these applications.

cs.GR↗

arXiv2014

A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials

We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected on to a basis of hyperspherical harmonics in four dimensions. The bispectrum components are the same bond-orientational order parameters employed by the GAP potential [arXiv:0910.1019]. The SNAP potential, unlike GAP, assumes a linear relationship between atom energy and bispectrum components. The linear SNAP coefficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum coefficients.

cond-mat.mtrl-sci↗