arXiv subjects

David Walz

Publications and source records attributed to David Walz.

17 recordsLinked to original sources

arXiv2025

CarAT: Carbon Atom Tracing across Industrial Chemical Value Chains via Chemistry Language Models

The chemical industry is increasingly prioritising sustainability, with a focus on reducing carbon footprints to achieve net zero. By 2026, the Together for Sustainability (TfS) consortium will require reporting of biogenic carbon content (BCC) in chemical products, posing a challenge as BCC depends on feedstocks, value chain configuration, and process-specific variables. While carbon-14 isotope analysis can measure BCC, it is impractical for continuous industrial monitoring. This work presents CarAT (Carbon Atom Tracker), an automated methodology for calculating BCC across industrial value chains, enabling dynamic and accurate sustainability reporting. The approach leverages existing Enterprise Resource Planning data in three stages: (1) preparing value chain data, (2) performing atom mapping in chemical reactions using chemistry language models, and (3) applying a linear program to calculate BCC given known inlet compositions. The methodology is validated on a 27-node industrial toluene diisocyanate value chain. Three scenarios are analysed: a base case with fossil feedstocks, a case incorporating a renewable feedstock, and a butanediol value chain with a recycle stream. Results are visualised with Sankey diagrams showing the flow of carbon attributes across the value chain. The key contribution is a scalable, automated method for real-time BCC calculation under changing industrial conditions. CarAT supports compliance with upcoming reporting mandates and advances carbon neutrality goals by enabling systematic fossil-to-biogenic substitution. Through transparent, auditable tracking of carbon sources in production networks, it empowers data-driven decisions to accelerate the transition to sustainable manufacturing.

cs.CE↗

arXiv2024

BoFire: Bayesian Optimization Framework Intended for Real Experiments

Our open-source Python package BoFire combines Bayesian Optimization (BO) with other design of experiments (DoE) strategies focusing on developing and optimizing new chemistry. Previous BO implementations, for example as they exist in the literature or software, require substantial adaptation for effective real-world deployment in chemical industry. BoFire provides a rich feature-set with extensive configurability and realizes our vision of fast-tracking research contributions into industrial use via maintainable open-source software. Owing to quality-of-life features like JSON-serializability of problem formulations, BoFire enables seamless integration of BO into RESTful APIs, a common architecture component for both self-driving laboratories and human-in-the-loop setups. This paper discusses the differences between BoFire and other BO implementations and outlines ways that BO research needs to be adapted for real-world use in a chemistry setting.

cs.LG↗

arXiv2023

Practical Path-based Bayesian Optimization

There has been a surge in interest in data-driven experimental design with applications to chemical engineering and drug manufacturing. Bayesian optimization (BO) has proven to be adaptable to such cases, since we can model the reactions of interest as expensive black-box functions. Sometimes, the cost of this black-box functions can be separated into two parts: (a) the cost of the experiment itself, and (b) the cost of changing the input parameters. In this short paper, we extend the SnAKe algorithm to deal with both types of costs simultaneously. We further propose extensions to the case of a maximum allowable input change, as well as to the multi-objective setting.

cs.LG↗

arXiv2023

Optimizing over trained GNNs via symmetry breaking

Optimization over trained machine learning models has applications including: verification, minimizing neural acquisition functions, and integrating a trained surrogate into a larger decision-making problem. This paper formulates and solves optimization problems constrained by trained graph neural networks (GNNs). To circumvent the symmetry issue caused by graph isomorphism, we propose two types of symmetry-breaking constraints: one indexing a node 0 and one indexing the remaining nodes by lexicographically ordering their neighbor sets. To guarantee that adding these constraints will not remove all symmetric solutions, we construct a graph indexing algorithm and prove that the resulting graph indexing satisfies the proposed symmetry-breaking constraints. For the classical GNN architectures considered in this paper, optimizing over a GNN with a fixed graph is equivalent to optimizing over a dense neural network. Thus, we study the case where the input graph is not fixed, implying that each edge is a decision variable, and develop two mixed-integer optimization formulations. To test our symmetry-breaking strategies and optimization formulations, we consider an application in molecular design.

math.OC↗

arXiv2022

Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization

Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical, sequential setting of Bayesian Optimization does not translate well into laboratory experiments, for instance battery design, where measurements may come from different sources and their evaluations may require significant waiting times. Multi-fidelity Bayesian Optimization addresses the setting with measurements from different sources. Asynchronous batch Bayesian Optimization provides a framework to select new experiments before the results of the prior experiments are revealed. This paper proposes an algorithm combining multi-fidelity and asynchronous batch methods. We empirically study the algorithm behavior, and show it can outperform single-fidelity batch methods and multi-fidelity sequential methods. As an application, we consider designing electrode materials for optimal performance in pouch cells using experiments with coin cells to approximate battery performance.

cs.LG↗

arXiv2022

Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces

Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. To address both points simultaneously, we propose using the kernel interpretation of tree ensembles as a Gaussian Process prior to obtain model variance estimates, and we develop a compatible optimization formulation for the acquisition function. The latter further allows us to seamlessly integrate known constraints to improve sampling efficiency by considering domain-knowledge in engineering settings and modeling search space symmetries, e.g., hierarchical relationships in neural architecture search. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.

stat.ML↗

arXiv2022

SnAKe: Bayesian Optimization with Pathwise Exploration

Bayesian Optimization is a very effective tool for optimizing expensive black-box functions. Inspired by applications developing and characterizing reaction chemistry using droplet microfluidic reactors, we consider a novel setting where the expense of evaluating the function can increase significantly when making large input changes between iterations. We further assume we are working asynchronously, meaning we have to select new queries before evaluating previous experiments. This paper investigates the problem and introduces 'Sequential Bayesian Optimization via Adaptive Connecting Samples' (SnAKe), which provides a solution by considering large batches of queries and preemptively building optimization paths that minimize input costs. We investigate some convergence properties and empirically show that the algorithm is able to achieve regret similar to classical Bayesian Optimization algorithms in both synchronous and asynchronous settings, while reducing input costs significantly. We show the method is robust to the choice of its single hyper-parameter and provide a parameter-free alternative.

cs.LG↗

arXiv2021

Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles

Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives, e.g. economic gain vs. environmental impact. Moreover, a large number of input variables and different variable types, e.g. continuous and categorical, are challenges commonly present in real-world applications. In some cases, proposed optimal solutions need to obey explicit input constraints related to physical properties or safety-critical operating conditions. This paper proposes a novel data-driven strategy using tree ensembles for constrained multi-objective optimization of black-box problems with heterogeneous variable spaces for which underlying system dynamics are either too complex to model or unknown. In an extensive case study comprised of synthetic benchmarks and relevant energy applications we demonstrate the competitive performance and sampling efficiency of the proposed algorithm compared to other state-of-the-art tools, making it a useful all-in-one solution for real-world applications with limited evaluation budgets.

stat.ML↗

arXiv2018

Production and propagation of ultra-high energy photons using CRPropa 3

In order to interpret cosmic ray observations, detailed modeling of propagation effects invoking all important messengers is necessary. We introduce a new photon production and propagation code as an inherent part of the CRPropa 3 software framework. By implementing additional photon production channels, which are important for energies below 10**18 eV, this code can be used for multi-messenger studies connecting the TeV and sub EeV energy regime and for interpreting models of ultra-high energy cosmic ray sources. We discuss the importance of the individual production channels and propagation effects and present example applications.

astro-ph.IM↗

arXiv2017

A Deep Learning-based Reconstruction of Cosmic Ray-induced Air Showers

We describe a method of reconstructing air showers induced by cosmic rays using deep learning techniques. We simulate an observatory consisting of ground-based particle detectors with fixed locations on a regular grid. The detector's responses to traversing shower particles are signal amplitudes as a function of time, which provide information on transverse and longitudinal shower properties. In order to take advantage of convolutional network techniques specialized in local pattern recognition, we convert all information to the image-like grid of the detectors. In this way, multiple features, such as arrival times of the first particles and optimized characterizations of time traces, are processed by the network. The reconstruction quality of the cosmic ray arrival direction turns out to be competitive with an analytic reconstruction algorithm. The reconstructed shower direction, energy and shower depth show the expected improvement in resolution for higher cosmic ray energy.

astro-ph.IM↗

arXiv2016

CRPropa 3 - a Public Astrophysical Simulation Framework for Propagating Extraterrestrial Ultra-High Energy Particles

We present the simulation framework CRPropa version 3 designed for efficient development of astrophysical predictions for ultra-high energy particles. Users can assemble modules of the most relevant propagation effects in galactic and extragalactic space, include their own physics modules with new features, and receive on output primary and secondary cosmic messengers including nuclei, neutrinos and photons. In extension to the propagation physics contained in a previous CRPropa version, the new version facilitates high-performance computing and comprises new physical features such as an interface for galactic propagation using lensing techniques, an improved photonuclear interaction calculation, and propagation in time dependent environments to take into account cosmic evolution effects in anisotropy studies and variable sources. First applications using highlighted features are presented as well.

astro-ph.IM↗

arXiv2015

Effects of uncertainties in simulations of extragalactic UHECR propagation, using CRPropa and SimProp

The results of simulations of the extragalactic propagation of ultra-high energy cosmic rays (UHECRs) have intrinsic uncertainties due to poorly known physical quantities and approximations used in the codes. We quantify the uncertainties in the simulated UHECR spectrum and composition due to different models for the extragalactic background light (EBL), different photodisintegration setups, approximations concerning photopion production and the use of different simulation codes. We discuss the results for several representative source scenarios with proton, nitrogen or iron at injection. For this purpose we used SimProp and CRPropa, two publicly available codes for Monte Carlo simulations of UHECR propagation. CRPropa is a detailed and extensive simulation code, while SimProp aims to achieve acceptable results using a simpler code. We show that especially the choices for the EBL model and the photodisintegration setup can have a considerable impact on the simulated UHECR spectrum and composition.

astro-ph.HE↗

arXiv2014

Cosmic ray propagation with CRPropa 3

Solving the question of the origin of ultra-high energy cosmic rays (UHECRs) requires the development of detailed simulation tools in order to interpret the experimental data and draw conclusions on the UHECR universe. CRPropa is a public Monte Carlo code for the galactic and extragalactic propagation of cosmic ray nuclei above $\sim 10^{17}$ eV, as well as their photon and neutrino secondaries. In this contribution the new algorithms and features of CRPropa 3, the next major release, are presented. CRPropa 3 introduces time-dependent scenarios to include cosmic evolution in the presence of cosmic ray deflections in magnetic fields. The usage of high resolution magnetic fields is facilitated by shared memory parallelism, modulated fields and fields with heterogeneous resolution. Galactic propagation is enabled through the implementation of galactic magnetic field models, as well as an efficient forward propagation technique through transformation matrices. To make use of the large Python ecosystem in astrophysics CRPropa 3 can be steered and extended in Python.

astro-ph.IM↗

arXiv2014

A field study of data analysis exercises in a bachelor physics course using the internet platform VISPA

Bachelor physics lectures on particle physics and astrophysics were complemented by exercises related to data analysis and data interpretation at the RWTH Aachen University recently. The students performed these exercises using the internet platform VISPA, which provides a development environment for physics data analyses. We describe the platform and its application within the physics course, and present the results of a student survey. The students acceptance of the learning project was positive. The level of acceptance was related to their individual preference for learning with a computer. Furthermore, students with good programming skills favor working individually, while students who attribute themselves having low programming abilities favor working in teams. The students appreciated approaching actual research through the data analysis tasks.

physics.ed-ph↗

arXiv2013

PARSEC: A Parametrized Simulation Engine for Ultra-High Energy Cosmic Ray Protons

We present a new simulation engine for fast generation of ultra-high energy cosmic ray data based on parametrizations of common assumptions of UHECR origin and propagation. Implemented are deflections in unstructured turbulent extragalactic fields, energy losses for protons due to photo-pion production and electron-pair production, as well as effects from the expansion of the universe. Additionally, a simple model to estimate propagation effects from iron nuclei is included. Deflections in galactic magnetic fields are included using a matrix approach with precalculated lenses generated from backtracked cosmic rays. The PARSEC program is based on object oriented programming paradigms enabling users to extend the implemented models and is steerable with a graphical user interface.

astro-ph.HE↗

arXiv2013

CRPropa 3.0 - a Public Framework for Propagating UHE Cosmic Rays through Galactic and Extragalactic Space

The interpretation of experimental data of ultra-high energy cosmic rays (UHECRs) above 10^17 eV is still under controversial debate. The development and improvement of numerical tools to propagate UHECRs in galactic and extragalactic space is a crucial ingredient to interpret data and to draw conclusions on astrophysical parameters. In this contribution the next major release of the publicly available code CRPropa (3.0) is presented. It reflects a complete redesign of the code structure to facilitate high performance computing and comprises new physical features such as an interface for galactic propagation using lensing techniques and inclusion of cosmological effects in a three-dimensional environment. The performance is benchmarked and first applications are presented.

astro-ph.IM↗

arXiv2012

A new Monte Carlo Generator for Ultra-High Energy Cosmic Rays from the Local and Distant Universe

For the understanding of the origin and propagation of ultra-high energy cosmic rays (UHECR) we developed a new approach to simulating UHECRs from an arbitrary number of sources based on Monte Carlo technique. The method consists of a combination of three steps. For distant sources we apply commonly accepted parameterizations to calculate the contribution to the observed cosmic ray flux. For sources of the local universe we use forward tracking through realistic matter distributions and magnetic fields resulting from explicit simulations of large-scale structure formation. From the calculations and the forward tracking we generate maps of the probability to observe a particle with a given energy from a discrete direction. To account for deflections in the galactic field, these probability maps are transformed by matrices calculated from backtracking of antiparticles through field parameterizations. Based on the combined probability maps, Monte Carlo production of individual UHECR data is performed which are then used in comparisons with UHECR measurements. The simulated UHECR data serves for optimizing the analysis techniques used in UHECR measurements as well as for constraining the parameter space of the underlying source and magnetic field models.

astro-ph.HE↗