From Quarks, Neutrinos and Neutron Stars to Evolutionary Algorithms Stephen Friess June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 1
Outline Part 1: Personal Background 1. Short Introduction 2. Optimization Problems in Theoretical Physics 3. Research Activities at the IKP TU Darmstadt 4. Summary and Personal Outlook Part 2: Paper Presentation 1. Introduction to Evolutionary Algorithms 2. Negatively Correlated Search 3. Computational Studies with NCS-C 4. Discussion and Outlook June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 2
Short Introduction Biographical Information: ◮ Name: Stephen Friess ◮ Born: 05.05.1990 ◮ Residence: Germany ◮ Degree: Master of Science (2016) ◮ Field: Theoretical Physics Previous Fields of Research: ◮ Strong QCD and Nuclear Astrophysics. Current Occupation: ◮ Software Developer and Consultant (GIP AG) ◮ Sector: Telecommunications (OSS systems) June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 3
Short Introduction Working in Telecommunications: ◮ Focus: Operations Support Systems. ◮ IT Systems for Processing and Technical Production of Telecommunication Services. ◮ Specifically: Fulfillment and Assurance Processes for Layer-2/3 based Services. ◮ Also: Backend for a Conferencing System. Further Education, Experiences and Achievements: ◮ 2017/18: Machine Learning MOOCs (Stanford University, etc.). ◮ 2017: IIoT Quest 2017 with startup idatase GmbH (1st place). ◮ 2015: Ludum Dare 33 & 34 Hackathons (1st and 2nd place @ KOM Lab). ◮ 2015: Teaching Assisstant for Computational Physics . ◮ Since 2013: Member of the German Physical Society (DPG eV). June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 4
Optimization Problems in Theoretical Physics Motion of a Single Particle in Classical Mechanics: ◮ Task: Determine trajectory � x = � x ( t ) from Newton’s Second Law : x = � m ¨ � F . ◮ Most forces � F can be derived from a potential energy V such that: � F = − � ∇ V ( � x ). ◮ In this case follows the Law of Energy Conservation : H = T + V = const . with kinetic energy T = 1 / 2 m ˙ x 2 � June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 5
Optimization Problems in Theoretical Physics ◮ In Theoretical Physics we take the Legendre transform of H p = 1 p · ˙ � L = � � x − H with � ∇ ˙ x H m ◮ We call L the Lagrangian . It can be rewritten as: L = T − V . ◮ I.e.: The difference of kinetic and potential energy. ◮ In Theoretical Physics we introduce the scalar action S: � t 1 x , ˙ S [ � dt L [ � � x ] = x , t ] t 0 ◮ Interpretation: Sum of energy differences from t 0 → t 1 . June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 6
Optimization Problems in Theoretical Physics ◮ Principle of Least Action: δ S [ � x ] = 0 ◮ Reformulation of particle motion as optimization problem. ◮ We want to ”learn” � x ( t ) such that the action S [ � x ] is minimized . ◮ Equivalent Euler-Lagrange Equation: � d ∂ − ∂ � x , ˙ L [ � � x , t ] = 0 dt ∂ ˙ x i ∂ x i ◮ The Principle of Least Action and the Lagrange Formalism form the basis of Modern Theoretical Physics. June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 7
Research Activities at the IKP TU Darmstadt Research Focus of the Theory Center: ◮ Nuclear Physics and Nuclear Astrophysics ◮ I.e.: Matter under Extreme Conditions. What do we learn from it: ◮ The Nature of the Nuclear Forces and Interactions. ◮ The Astrophysical Effects of Nuclear Reaction Theories. Previously associated Research Groups: ◮ Theoretical Nuclear Astrophysics Group (Master’s Thesis) ◮ Nuclei, Hadrons & Quarks Group (Bachelor’s Thesis) June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 8
Research Activities at the IKP TU Darmstadt Bachelor’s Thesis at the NHQ Group: ◮ Focus: Investigation of Thermodynamic Properties of the Quark-Gluon Plasma . ◮ Problem: Exact Lagrangian L QCD is computationally difficult to access. ◮ Use of approximation schemes, so called ’ Effective Field Theories ’. ∂ − m ) ψ + G S [( ψψ ) 2 + ( ψ i γ 5 � L QCD ≈ ψ ( i / τψ ) 2 ]+ G V ( ψγ µ ψ ) 2 My Research : ◮ Testing the performance of novel contributions to an existing theory, in contrast to data from computationally ’exact’ calculations. June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 9
Research Activities at the IKP TU Darmstadt Master’s Thesis at the Theoretical Nuclear Astrophysics Group: ◮ Focus: Nucleosynthesis , Effects of Nuclear Physics on Stellar Evolution and vice versa the Effects of Astrophysical Conditions on Nuclear Reactions . ◮ My Area of Research: Neutrino Reactions in Proto-Neutron Stars. ◮ Neutrino reaction rates are specifically governed by matrix elements M if M if ∼ L weak = G F 2 H µ L µ ◮ Thus, dependent upon a Lagrangian L weak describing the reaction. June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 10
Summary A brief summary: ◮ The Principle of Least Action reformulates Modern Theoretical Physics as an Optimization Problem. ◮ Consider a marble rolling down a hilly landscape from point A to B. Naturally it takes the path of least action. ◮ Euler-Lagrange Equations arise as a direct consequence from this principle. ◮ Modern Theoretical Physics is concerned with the study of Lagrangians . Especially their mathematical properties and observables arising from their structure. June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 11
Personal Outlook ◮ An exciting time to be a scientist: Progresses in mathematical theories and methods lead to technological innovations faster than ever. ◮ As a theoretical physicist: I combine broad mathematical expertise and insight with an application-oriented mindset. ◮ My personal outlook : By becoming a PhD student, I look forward to further advance my diverse set of skills and interdisciplinary interests and contribute in cutting-edge research with real-world applications. June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 12
Outline Part 1: Personal Background 1. Short Introduction 2. Optimization Problems in Theoretical Physics 3. Research Activities at the IKP TU Darmstadt 4. Personal Outlook Part 2: Paper Presentation 1. Introduction to Evolutionary Algorithms 2. Negatively Correlated Search 3. Computational Studies with NCS-C 4. Discussion and Outlook June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 13
Introduction to Evolutionary Algorithms General Concept (Simon 2013): An algorithm that evolves a problem solution over many iterations. There exists not a more precise and generally agreed upon definition. However, one finds important and recurring ingredients (Eiben & Smith 2015): ◮ An objective function which is minimized. ◮ A population of candidate solutions with fixed size. ◮ A parent selection mechanism and variation operators . ◮ A survivor selection mechanism . June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 14
Introduction to Evolutionary Algorithms General scheme of an EA in pseudo code: June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 15
Introduction to Evolutionary Algorithms Some important remarks: ◮ Evolutionary Algorithms are non-deterministic and thus stochastic. ◮ Evolutionary Algorithms can also be non-nature inspired. ◮ Natural evolution is in fact not an optimizing process (e.g. genetic drifts). Application Areas for Evolutionary Algorithms: ◮ Data Science: Data Analysis, training of DNNs, tuning of SVMs. ◮ Technology & Engineering: Structural Engineering, Antenna Design, etc. June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 16
Negatively Correlated Search Proposed by Ke Tang, Peng Yang and Xin Yao in 2016: ◮ Population-based search method utilizing multiple Random Local Searches which are run in parallel. Distinguishing Feature of NCS: ◮ Inspired by cooperation in human behaviour. ◮ Information is shared among searching ’agents’ to encourage different searching behaviours which focus on uncovered regions. June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 17
Negatively Correlated Search General Framework: ◮ Each search process i is considered to be a RLS, which creates candidate solutions using a probability distribution p i ( x ) as a generator of variance. ◮ To ensure variety in exploration, we need a measure of similarity between distributions p i and p j . Thus, we use the Bhattacharyya distance as defined by: �� � � D B ( p i , p j ) = − ln d x p i ( x ) p j ( x ) ◮ Eventually, we want the Bhattacharyya distance from p i to all p j to be maximal, or equivalently the term: j { D B ( p i , p j ) | j � = i } Corr( p i ) = min June 26, 2018 | TU Darmstadt, IKP , Theoretical Nuclear Astrophysics Group | Stephen Friess | 18
Recommend
More recommend
