Faculty

Institution

Unit

Venue

Christopher J. Earls

J Preston Levis Professor of Engineering

Cornell University

Affiliations

College of Engineering

School of Civil and Environmental Engineering

Christopher J. Earls is currently serving as a College Of Engineering J Preston Levis Professor of Engineering at Cornell University.

Honors

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Experiences

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Education

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Christopher J. Earls

Cornell University

J Preston Levis Professor of Engineering

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structural engineering engineering structures engineering structural mechanics structural safety mechanical engineering applied mathematics civil engineering structural steel structural analysis finite element method in structural mechanics steel research structural optimization structural design bridge engineering structural performance mechanics of materials bridge design inverse problems scientific computing structural materials structural applications machine learning numerical simulations fracture mechanics machine learning research structural identification deep learning bayesian analysis finite element methods computational intelligence composite structures physic aware machine learning shell structures mechanical science marine engineering naval architecture blast engineering blast loading pde-constrained optimization marine structures shell theory crack formation dynamic crack propagation deformation reconstruction radar applications thin-walled structure structural ductility structural reliability radar signal processing high strength low alloy steel condition monitoring isogeometric analysis bayesian hierarchical modeling corrosion technology bayesian statistics approximate bayesian computation steel i-girder bridge blast effects geometric learning numerical method for partial differential equation musculoskeletal function sequential monte carlo data-driven discovery rational neural networks web compactness compact i-shaped beams effective width web compactness criteria design recommendations precision immobilization technique single angle members robot operating system network traffic security exploits inelastic failure network science natural computing applied intelligent system technology informatics intelligent systems artificial intelligence memetic computing applied artificial intelligence

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

Pde-Learn: Using Deep Learning to Discover Partial Differential Equations From Noisy, Limited Data. Neural Networks (2024)

Robert StephanyChristopher Earls

computer sciencegeometric learningphysic aware machine learningnonlinear analysisdeep learningpde-constrained optimizationmachine learning researchpartial differential equationsdata sciencecomputational intelligenceusing deep learningapplied mathematicsmachine learning

Weak-Pde-Learn: A Weak Form Based Approach to Discovering Pdes From Noisy, Limited Data. Journal of Computational Physics (2024)

Robert StephanyChristopher Earls

computer scienceknowledge discoverynoisy dataregularization (mathematics)pde-constrained optimizationweak formdata miningpartial differential equationsdata sciencemachine learning researchapplied mathematicsmachine learninghigh-dimensional statistics

Principled Interpolation of Green’s Functions Learned From Data. Computer Methods in Applied Mechanics and Engineering (2023)

Harshwardhan PraveenNicolas BoulléChristopher Earls

geometric interpolationboundary value problemsnumerical analysisinterpolation spacescomputational mathematicsradial basis functionsnumerical mathematicspartial differential equationsdata scienceapproximation theoryapplied mathematicsgreen ’

A New Engineering Theory Describing Oblique Free Surface Impact by Flexible Plates. Ocean Engineering (2022)

Wensi WuChristopher Earls

structural crashworthinesssolid mechanicsstructural mechanicsimpact (mechanics)deformationengineeringflexible platestribologymechanical engineeringmechanical sciencenew engineering theoryengineering structuresimpact loadingmechanics of materialscontact mechanicsmechanicsstructural optimization

Bayesian Deep Learning for Partial Differential Equation Parameter Discovery With Sparse and Noisy Data. Journal of Computational Physics X (2022)

Christophe BonnevilleChristopher Earls

bayesian deep learningcomputer sciencephysic aware machine learningdeep learningsparse neural networkbayesian neural networksscientific computingbayesian analysisneural network (machine learning)pde discoverymachine learning researchinverse problemsdata sciencecomputational intelligenceapplied mathematicsmachine learning

Data-Driven Discovery of Green’s Functions With Human-Understandable Deep Learning. Scientific Reports (2022)

Nicolas BoulléChristopher J. EarlsAlex Townsend

rational neural networksgeometric learningphysic aware machine learningdata-driven discoverydeep learningscientific computingneural network (machine learning)machine learning researchcomputational intelligencedata scienceapplied mathematicsmachine learning

Inverse Modelling of Potential Measurements on Reinforced Concrete to Assess Localized Corrosion: Sensitivity With Respect to the Corrosion State and Concrete Properties. e-Journal of Nondestructive Testing (2022)

Meeke van EdeChristopher J. EarlsAndreas FichtnerUeli Angst

corrosion technologycorrosion rateinverse modellingcorrosion resistanceengineeringstructural health monitoringcivil engineeringcorrosionlocalized corrosion

Pde-Read: Human-Readable Partial Differential Equation Discovery Using Deep Learning. Neural Networks (2022)

Robert StephanyChristopher Earls

physic aware machine learningboundary value problemsnonlinear analysisdeep learningrecurrent neural networksscientific computingpde-constrained optimizationneural network (machine learning)machine learning researchpartial differential equationsdata sciencecomputational intelligenceapplied mathematicsmachine learning

A Principled Approach to Design Using High Fidelity Fluid-Structure Interaction Simulations. Finite Elements in Analysis and Design (2021)

Wensi WuChristophe BonnevilleChristopher Earls

multiscale hydrodynamicsnumerical simulationsengineeringfluid dynamicsphysical prototypingmechanical engineeringfluid flowcomputational fluid dynamicsfluid-solid interactionsfluid mechanicsmultiphysics problemsfluid-structure interactiondesigncomputer-aided designprincipled approachmulti-physics modellingstructural optimization

Bayesian Inference Approaches for the Detection and Characterization of Hidden Pitting Corrosion. Mechanical Systems and Signal Processing (2021)

Andrew LoebChristopher Earls

bayesian statisticshidden pitting corrosioninferencebayesian inferencebayesian inference approachesstatistical inferencebayesian analysis

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Christopher J. Earls

J Preston Levis Professor of Engineering

Cornell University

Affiliations

College of Engineering

School of Civil and Environmental Engineering

Christopher J. Earls is currently serving as a College Of Engineering J Preston Levis Professor of Engineering at Cornell University.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Christopher J. Earls

Cornell University

J Preston Levis Professor of Engineering

You can click a keyword to filter the displayed items

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

Pde-Learn: Using Deep Learning to Discover Partial Differential Equations From Noisy, Limited Data. Neural Networks (2024)

Robert StephanyChristopher Earls

Weak-Pde-Learn: A Weak Form Based Approach to Discovering Pdes From Noisy, Limited Data. Journal of Computational Physics (2024)

Robert StephanyChristopher Earls

Principled Interpolation of Green’s Functions Learned From Data. Computer Methods in Applied Mechanics and Engineering (2023)

Harshwardhan PraveenNicolas BoulléChristopher Earls

A New Engineering Theory Describing Oblique Free Surface Impact by Flexible Plates. Ocean Engineering (2022)

Wensi WuChristopher Earls

Bayesian Deep Learning for Partial Differential Equation Parameter Discovery With Sparse and Noisy Data. Journal of Computational Physics X (2022)

Christophe BonnevilleChristopher Earls

Data-Driven Discovery of Green’s Functions With Human-Understandable Deep Learning. Scientific Reports (2022)

Nicolas BoulléChristopher J. EarlsAlex Townsend

Meeke van EdeChristopher J. EarlsAndreas FichtnerUeli Angst

corrosion technologycorrosion rateinverse modellingcorrosion resistanceengineeringstructural health monitoringcivil engineeringcorrosionlocalized corrosion

Pde-Read: Human-Readable Partial Differential Equation Discovery Using Deep Learning. Neural Networks (2022)

Robert StephanyChristopher Earls

A Principled Approach to Design Using High Fidelity Fluid-Structure Interaction Simulations. Finite Elements in Analysis and Design (2021)

Wensi WuChristophe BonnevilleChristopher Earls

Bayesian Inference Approaches for the Detection and Characterization of Hidden Pitting Corrosion. Mechanical Systems and Signal Processing (2021)

Andrew LoebChristopher Earls

bayesian statisticshidden pitting corrosioninferencebayesian inferencebayesian inference approachesstatistical inferencebayesian analysis

1
2
3
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5
13
10 / page

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sustainable infrastructure systems

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computational stochastic dynamics

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