Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process

Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces to discover optimal experimental conditions and regions of interest in the image space or parameter space of computational models. The direct grid search of the parameter space tends to be extremely time-consuming, leading to the development of strategies balancing exploration of unknown parameter spaces and exploitation towards required performance metrics. However, classical Bayesian optimization strategies based on the Gaussian process (GP) do not readily allow for the incorporation of the known physical behaviors or past knowledge. Here we explore a hybrid optimization/exploration algorithm created by augmenting the standard GP with a structured probabilistic model of the expected system’s behavior. This approach balances the flexibility of the non-parametric GP approach with a rigid structure of physical knowledge encoded into the parametric model. The fully Bayesian treatment of the latter allows additional control over the optimization via the selection of priors for the model parameters. The method is demonstrated for a noisy version of the classical objective function used to evaluate optimization algorithms and further extended to physical lattice models. This methodology is expected to be universally suitable for injecting prior knowledge in the form of physical models and past data in the Bayesian optimization framework.

Applying machine learning to study fluid mechanics

This paper provides a short overview of how to use machine learning to build data-driven models in fluid mechanics. The process of machine learning is broken down into five stages: (1) formulating a problem to model, (2) collecting and curating training data to inform the model, (3) choosing an architecture with which to represent the model, (4) designing a loss function to assess the performance of the model, and (5) selecting and implementing an optimization algorithm to train the model. At each stage, we discuss how prior physical knowledge may be embedding into the process, with specific examples from the field of fluid mechanics. Graphic abstract

VIII International Conference Days of Applied Mathematics

These proceedings are the contributions made by the participants of the VIII International Conference / Days of Applied Mathematics (ICDAM). The main objective of the VIII ICDAM is to effectively share and transfer mathematical and physical knowledge for the purpose of problem-solving in different contexts of the sciences, engineering, and medicine. The VIII ICDAM was organized by the Facultad de Ciencias Ciencias Básicas y Biomédicas of the Universidad Simón Bolívar, was carried out in the city of San José de Cúcuta, Colombia, from October 20-22, 2021, in which dissertations and workshops developed by students, professors and researchers, those who worked in research lines related to the field of mathematics and physics from Francia, Mexico, República Dominicana, Chile, Argentina, Venezuela, and Colombia. List of Organizing Committee, National Scientific Committee, International Scientific Committee, Sponsor, Partners are available in this pdf.

Methodology of applying structural and logical schemes in the study of general physics in a technical university

A structural and logical scheme is a visual image of the logical connection of the main elements of knowledge within the framework of a training course, section or topic. When studying physics as an applied discipline in a technical university, its professional orientation and applied knowledge corresponding to this function come out in the first place. But applied knowledge as a consequence of physical theories is not enough for the development of a modern quantum-relativistic worldview. The idea of our research is to precede the systematic study of general physics with systematic ideas about the place and meaning of each physical theory, namely: before studying classical physics, to show its connection with quantum and relativistic physics. To do this, it is necessary to apply a preliminary and final generalization at different stages of the study of physics with the help of appropriate structural and logical schemes. When implementing this idea, the following methods were used: the method of structural and logical analysis of the course of general physics with the allocation of knowledge elements, the method of systematization based on clarifying the connection between physical theories and the method of generalization, leading to the construction of new generalized schemes of this course. In the proposed schemes "Connection of mechanical theories" and "Scales of the Universe-Velocities", we identify structural elements that reveal the specifics of the methodological representations of the theory in accordance with its place in the Universe and the velocities of its objects. The proposed methodology is based on two types of generalization: preliminary and final. The preliminary generalization shows the place of physical theory in the system of physical knowledge in the course of general physics, the final generalization is used to make students aware of the specifics of the entire range of methodological concepts used in this physical theory. The methodology is aimed at forming students ' systematic knowledge of general physics and at developing their modern quantum-relativistic worldview.

THE TRADITION OF ALCHEMICAL AND ASTROLOGICAL KNOWLEDGE WITHIN AND AROUND THEOPHAN PROKOPOVYCH’S LIBRARY

The important role of the alchemical and astrological tradition in the formation and trans-formation of science as a social institution in the Early Modern period is researched in detail in Western historiography of science. At the same time, the Ukrainian aspect of this pan-European phenomenon needs further intensive study.The article deals with the alchemical and astrological component of Ukrainian science of the High Baroque era on an example of Theophan Prokopovych (1677 – 1736). The analysis of the ca¬talog of Prokopovych’s library confirmed that the alchemical-astrological and magical-physical knowledge belonged to the sphere of interests of the scholar. His activity, in addi-tion to cosmogonic reasoning and mathematical calculations, also had a practical compo-nent. Books from the library’s holdings included works of late alchemy, which allowed Pro-kopovych to be aware of the latest ideas, trends, and achievements in this and related fields of knowledge. This is reflected in the formation of the worldview and creative work of the scholar.A comparison of the facts of biographies, the essence and direction of creativity, and the relationship of the authors mentioned in Prokopovych’s treatise “Natural Philosophy or Physics”, testified to the existence of the united pan-European scientific and information space, within which the tradition of late alchemy was formed and transformed during the 16th-18th centuries. Theophan Prokopovych also belonged to this tradition, and his works reflected the state and essence of Ukrainian alchemical knowledge of the High Baroque era. Prokopovych’s own views on problems of alchemy and astrology are a topic of special re-search.

THE ONTOLOGICAL TRIANGLE OF THE RELATIONAL PARADIGM

The philosophical analysis of three main paradigms in the basis of physical knowledge is carried out. It is permissible to conclude that in the case of electromagnetic interaction between the emitter and the absorber: 1) the process of interaction of the photon with the medium in space and time can occur; 2) in the case when the photon “teleports” - there is only a relation outside of space and time. The following classification of fundamental concepts, with which the relational paradigm deals, is revealed. The ideal: space and time, field, information, a set of movements of quantum particles. The material: interactions, environment. Nothing more than countable: time, electromagnetic interactions. Uncountable: space, environment, interactions with the environment, a set of movements of quantum particles. Substantial: environment, interactions, information, a set of movements of quantum particles. Relational: space, time, field - as a means of description.

EFFECT OF TEACHING AND LEARNING-SCRUM ON IMPROVEMENT PHYSICS ACHIEVEMENT AND TEAM COLLABORATION ABILITY OF LOWER-SECONDARY SCHOOL STUDENT

Employers believe that people with the ability to work in teams can bring success to their business. Therefore, it is very essential to start cultivating students' teamwork skills in lower-secondary school to prepare students for the future. This study took "Physics in Bicycles" as an example to explore the effect of Teaching and Learning-Scrum (TL-Scrum) on students' physics achievement and team collaboration ability. It was conducted at a lower-secondary school in Changsha, China. "Physical Knowledge of Bicycles" Test and "Team Collaboration Ability" Measurement were applied to the two groups prior to and following the experiment. The experimental group (N=61) participated in TL-Scrum teaching, whereas the control group (N=58) participated without TL-Scrum teaching. The results revealed a significant difference between the two groups, with the experimental group learners performing better than the control group in the academic achievement. In addition, the results showed better positive effects of TL-Scrum on experimental group learners in team collaboration ability. Results suggested that learners achieved better academic achievements and team collaboration with the approach of TL-Scrum, which pointed to certain implications for physics teaching research, as well as in education of future physics teachers. Keywords: lower-secondary school students, physics education, team collaboration, TL-Scrum

Physics Informed Neural Networks Based on a Capacitance Resistance Model for Reservoirs Under Water Flooding Conditions

In recent years great interest has risen towards surrogate reservoir models based on data-driven methodologies with the purpose of speeding up reservoir management decisions. In this work, a Physics Informed Neural Network (PINN) based on a Capacitance Resistance Model (CRM) has been developed and tested on a synthetic and on a real dataset to predict the production of oil reservoirs under waterflooding conditions. CRMs are simple models based on material balance that estimate the liquid production as a function of injected water and bottom hole pressure. PINNs are Artificial Neural Networks (ANNs) that incorporate prior physical knowledge of the system under study to regularize the network. A PINN based on a CRM is obtained by including the residual of the CRM differential equations in the loss function designed to train the neural network on the historical data. During training, weights and biases of the network and parameters of the physical equations, such as connectivity factors between wells, are updated with the backpropagation algorithm. To investigate the effectiveness of the novel methodology on waterflooded scenarios, two test cases are presented: a small synthetic one and a real mature reservoir. Results obtained with PINN are compared with respect to CRM and ANN alone. In the synthetic case CRM and PINN give slightly better quality history matches and predictions than ANN. The connectivity factors estimated by CRM and PINN are very similar and correctly represent the underlying geology. In the real case PINN gives better quality history matches and predictions than ANN, and both significantly outperform CRM. Even though the CRM formulation is too simple to predict the complex behavior of a real reservoir, the CRM based regularization contributes to improving the PINN predictions quality compared to the purely data-driven ANN model. The connectivity factors estimated by CRM and PINN are not in agreement. However, the latter method provided results closer to our understanding of the flooding process after many years of operations and data analysis. All considered, PINN outperformed both CRM and ANN in terms of predictivity and interpretability, effectively combining strengths from both methodologies. The presented approach does not require the construction of a 3D model since it learns directly from production data, while preserving physical consistency. Moreover, it represents a computationally inexpensive alternative to traditional full-physics reservoir simulations which could have vast applications for problems requiring many forward evaluations, like the optimization of water allocation for mature reservoirs.

THE BEGINNING OF THE UNIVERSE AS AN EPISTEMOLOGICAL FRONTIER- LEMAÎTRE TO GAMOW

It was in 1922, when Alexandre Friedmann proposed some models for cosmic evolution, that modern cosmology faced for the first time in a scientific way the problem of the origin of the universe. It was the inaugural step of the big bang cosmology (usually known as the Big Bang Theory), to which several important cosmologists contributed over the following decades. Among these cosmologists, there were two who played a special role: Georges Lemaître, who proposed the primeval atom theory, and George Gamow, who later assumed the hot and dense primordial state of the universe which contemporary cosmology continues to admit. In this paper, I present and compare the perspectives of these two great cosmologists towards the idea of the beginning of the universe as an epistemological frontier, that is, as an unsurpassable limit to the physical knowledge of the universe, namely with regard to an explanation of what caused this beginning and how the primordial universe had come into existence. Both cosmologists assumed that the beginning of our universe is located before everything that physics can achieve, but we can identify one important difference: according to Lemaître, the beginning of the universe is located before space and time, and we can admit that is an epistemological beginning and also an ontological beginning; according to Gamow, the beginning of our universe may have been the result of a preexistent cosmological state of the universe which is just inaccessible to physics, and therefore is not an ontological but just an epistemological beginning.

Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning

This paper presents a novel approach for improving the safety of vehicles equipped with Adaptive Cruise Control (ACC) by making use of Machine Learning (ML) and physical knowledge. More exactly, we train a Soft Actor-Critic (SAC) Reinforcement Learning (RL) algorithm that makes use of physical knowledge such as the jam-avoiding distance in order to automatically adjust the ideal longitudinal distance between the ego- and leading-vehicle, resulting in a safer solution. In our use case, the experimental results indicate that the physics-guided (PG) RL approach is better at avoiding collisions at any selected deceleration level and any fleet size when compared to a pure RL approach, proving that a physics-informed ML approach is more reliable when developing safe and efficient Artificial Intelligence (AI) components in autonomous vehicles (AVs).