Algorithms based on adjoint function ensembles for inverse modeling - - PowerPoint PPT Presentation

SLIDE 1

Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk State University

Algorithms based on adjoint function ensembles for inverse modeling of transport and transformation of atmospheric pollutants

A.V. Penenko

CITES 2019, Moscow, 3-6 June 2019

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• Unified approach to different measurement data including image-type

measurement data in air quality applications (large volume of data with unknown value w.r.t. the considered inverse modelling task):

• Time-series (in situ)
• Vertical concentration profiles (aircraft sensing, lidar profiles, etc).
• Satellite images (total column 2D images).

Motivation

• The progress in the parallel computations technologies: the speedup is

achieved trough the intensive parallelization (ensemble algorithms, splitting, decomposition, etc.)

• Variety of applications for the inverse and data assimilation problems for

advection-diffusion-reaction models. E.g.

• Air quality studies (environmental applications)
• Morphogen theory (developmental biology)
• The progress in nonlinear ill-posed operator equation solution (different

regularization methods, SVD, convergence theory, etc.) and analysis methods

2

SLIDE 3

Advection-diffusion-reaction model

3

 

 

diag ( , , ) = ( , , ) ,

l l l l l l l l l

P t t f t r                 y u φ φ y = , , = 0,

l l

t    x

The domain

 

 

diag = , ( , ) (0, ],

R l l l l l

• ut

t T            n x

[0, ]

T

T   

= , ( , ) (0, ],

D l l in

t T      x

BC:

 

: , , R Y        y φ r a

Direct problem

• perator

advection-diffusion destruction-production

Subspace

mes

SpanU

= [ , Pr

Umes



   ( ) ( )

I y ] r φ I,

Inverse problem IC:  rectangular in (0D,)1D,2D Linear measurement

• perators, e.g.

Given Noise To find (or)

• Pointwise concentrations
• Total column 2D images
• Vertical profiles

Model scale: 0D,1D,2D

1, ,

c

l N  K

• number of species

SLIDE 4

Adjoint problem

   

2 1

, = , [ ] , ( , , , , ) [ ]

R Y

K t  



  

(2) (1)

h φ h φ y φ y δr h y

"

( ) = 0, T Ψ

 Lagrange type identity (sensitivity relation)

• divided

difference operator

       

2 1 1

( ( ) ) ( ( , , , , ) ) = ,

l l l l l

diag G t h t         

2

u φ y φ y Ψ

           

 

 

     

 

 

 

     

 

2 1 1 2 1 1 1 1 1

( , , , , ) , , , , ; , , ; , G t diag P t P t diag t

 

   

2 2 2 2

φ y φ y φ y φ φ y φ φ φ y

+ adjoint problem boundary conditions TC:

         

1 1 (2) (1) (2) (1)

( , , , , ) = ( , ; , ) ( , ; , ) ( ),

y y

K t t P t diag    

2 2 2 (2) (1)

φ y φ y φ y y φ y y φ

" " " ( ) ( ) ( )

[ , ]

m m m

 φ φ r y

Sensitivity functions Adjoint problem: Given find :

   

, , , 1,2,

m m m 

h φ y

Linear parametrizations

m m m

r r     , = , [ ]

m m R m

r   







h φ h

Ψ

4

SLIDE 5

Gradient algorithms

(inverse source problem)

( ) [ , , ], J    r r r h

Given the cost function if the parameters are smooth enough, then E.g. Polak-Ribiere conjugate gradient algorithm implemented in GSL

             

 

>0

:= = arg min

k k

J



      

k+1 k k k k

r r s r s

 

2

2 ( )

= [ ] .

T

l l l L l Lmes

J I  

 





r r

 

=1

2 [ ] , = 0,

Nc l l mes mes l

I l L l L                 r h

                         

, , >1 = , = , = ( ). , , =1

k k r

k J k          

k k k-1 k k-1 k k k k-1 k-1 k

g g g g s s g r g g g

[ ] φ r

5

Penenko, V. V. & Obraztsov, N. N. A variational initialization method for the fields of the meteorological elements // English translations Soviet Meteorology and Hydrology, 1976 , 11 , 3-16 Пененко, В. В. Методы численного моделирования атмосферных процессов Гидрометеоиздат, 1981 Dimet, F.-X. L. & Talagrand, O. Variational algorithms for analysis and assimilation of meteorological

• bservations: theoretical aspects // Tellus, 1986 , 38A , 97-110

4DVAR uncertainty state

SLIDE 6

Сonsider a splitting scheme on the interval ([Gordeziani,Meladze, 1974], [Samarski, Vabishevich, 2003]),

Sequential Data Assimilation at the Splitting Stages

6 1 j j

t t t

  

1. { , , } c x y z  

 

 

 

 

1 1 1

[0 ] [ ] { , , }

j j j j

L f r t {x t} X t t t t x y z

       

      

              

              

     

 

1 1

( , ) ( , )

c l c l c c l l c c l c l j j c

P t t t f r t t        

             

          r r

{ , , , }

( ) ( ).

j j x y z c

t t

  

  



 

• Transport
• Transformation

Next step

1 j

t 

      

Variational DA WRT Variational DA WRT

l

r

l

r

Measurements I

Penenko, A. et al. Sequential Variational Data Assimilation Algorithms at the Splitting Stages of a Numerical Atmospheric Chemistry Model // Large-Scale Scientific Computing, Springer International Publishing, 2018 , 536-543 Penenko, A.; Mukatova, Z. S.; Penenko, V. V.; Gochakov, A. & Antokhin, P. N. Numerical study of the direct variational algorithm of data assimilation in urban conditions // Atmospheric and ocean optics, 2018 , 31 , 456-462

Direct Algorithm

SLIDE 7

Sensitivity relation based

• Coarse-fine mesh method (Sequential solution refinement with sequential adjoint

problems solving) [Hasanov, A.; DuChateau, P. & Pektas, B. An adjoint problem approach and

coarse-fine mesh method for identification of the diffusion coefficient in a linear parabolic equation// Journal of Inverse and Ill-Posed Problems, 2006 , 14 , 1-29]

• Adjoint function for each measurement datum with the solution of the resulting
• perator equation [Marchuk G. I., On the formulation of certain inverse problems, Dokl. Akad.

Nauk SSSR, 156:3 (1964), 503–506], [Issartel, J.-P. Rebuilding sources of linear tracers after atmospheric concentration measurements // Atmospheric Chemistry and Physics, Copernicus GmbH, 2003 , 3 , 2111-2125]

Cost function based

• Cost functional gradients with adjoint problem solution (single element ensemble for

the discrepancy)

• Gradient computation with adjoint ensemble when adjoint is independent of direct

solution [Karchevsky, A., Eurasian journal of mathematical and computer applications, 2013 , 1 ,

4-20]

• Representer method (optimality system decomposition, ensemble generated for

discrepancies for each measurement data) [Bennett, A. F. Inverse Methods in Physical

Oceanography (Cambridge Monographs on Mechanics) Cambridge University Press, 1992]

Adjoint problem solution ensembles in inverse problem algorithms

7

SLIDE 8

Sensitivity operator

(inverse source problem)

Sensitivity relation (Lagrange type identity)

 

meas

U SpanU



 

(ξ)

u

Given functions

 

(2) (1) (2) (1) ( ) ( )

[ ] [ ] [ ] [ ]

U

H

  

   



φ r φ r φ r φ r u e

1

(2) (1) ( ) ( )

[ ] [ ] [ ]

 

       

(2) (1) (2) ( )

φ r φ r u r r u r r Image (model) to structure

• perator [Dimet et al,2015]

Sensitivity

• perator

( ) ( )

[ ] , [ ]

U

R M

    

          



(2) (1) (2) (1)

r r z Ψ r r u z e  a

The inverse problem solution for any and satisfy

 



r

r

U

   

 

[ ] = [ , ] . Pr

U U U Umes

H M H 

 

         I φ r r r r r I

Parametric family of quasi- linear operator equations Parallel w.r.t. U



8

SLIDE 9

Adjoint ensemble construction

 

= | [0, ], [0, ], [0, ], ,

x x y y t t mes

U L    



      

ηθ θ θ x y t

e

• Fourier cos-basis
• Wavelets, curvlets, etc. [Dimet et al.,2015]

«a priori» approach – ensemble for the class of problems (smoothness)

=1

1 ( , , ) ( , , ) ( , , ), = = , 0,

Nc k t x i y j x y t l

C T t C X x C Y y l e l

   

                   

2 cos , > 0 1 ( , , ) = . 1, = 0 t C T t T T               

«a posteriori» approach – ensemble for the considered problem

• «Adaptive basis»: chose elements of

with maximal projections

Penenko, A. V.; Nikolaev, S. V.; Golushko, S. K.; Romashenko, A. V. & Kirilova, I. A. Numerical Algorithms for Diffusion Coefficient Identification in Problems of Tissue Engineering // Math. Biol. Bioinf., 2016 , 11 , 426-444 (In Russian)

• «Informative basis»: Use left singular vectors of the operator

[ , ]

U

m



(0) (0)

r r

U

[ ] , Pr

Umes



(0) ηθ θ θ x y t

φ r I e

Penenko, A.; Zubairova, U.; Mukatova, Z. & Nikolaev, S. Numerical algorithm for morphogen synthesis region identification with indirect image-type measurement data // Journal of Bioinformatics and Computational Biology, World Scientific Pub Co Pte Lt, 2019 , 17 , 1940002

(inverse source problem, 2D, components are measured ) Defined by the adjoint problem sources sets

9

SLIDE 10

Inversion algorithm

 

 

 

 

 

 

 

, [ , ] [ ] = , P ] , [ r

U U Um U U s U e

H m H m m            

* * *

q φ q q q q q q I q q q δI q

 

, = . Pr ,

T T unknowns U U T T mes unknowns

m mm N H I m m m N

   

                                  δq φ q

 

C

  -truncated SVD inversion parametrized by conditional number

, inversesourceproblem , inversecoefficient problem

src t x y unknowns coeff

L N N N N N         

Newton- Kantorovich

• type update

[ , ]

U

m m q q  ( )

unknowns

N 

Nonlinearity: sequential increase of the conditional number Admissible solutions: projection regularization Noise: discrepancy principle Optional monotonicity: monotonic decrease of the discrepancy Theoretical foundations: [Issartel, J.-P., 2003], [Cheverda V.A., Kostin V.I., 1995], [Kaltenbacher et al, 2008], [Vainikko, Veretennikov, 1986]

10

SLIDE 11

Inverse source problem (0D)

1 2 3 3 2 2 2 1 2 3 3 2 3 1 1 2 2 3 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 3

hv NO NO O P hv O O D O HCHO hv CO 2HO HCHO hv CO H O O P O N O D N2 O P O D O O O P H O O D 2OH HO NO NO OH NO O NO O NO RO HCHO HO NO CO OH CO HO HC OH H O RO HCHO OH CO H O HO NO OH HNO 2H                                            

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

O H O O H O 2HO H O H O O HO RO O ROOH 2RO HCHO HO OH SO HO SULF.              

 

2 3

= ,

mes

L CO O

=10 3600 T  =3000

t

N =22

c

N

(0)

r 

Modified [Stockwell,2002]

Penenko, A. V. A Newton–Kantorovich Method in Inverse Source Problems for Production-Destruction Models with Time Series-Type Measurement Data // Numerical Analysis and Applications, 2019 , 12 , 51-69

Relative error for different number

• f projection functions

Source reconstruction

11

SLIDE 12

Larger ensembles and better solutions (0D)

• Lorenz’63 model,
• Inverse coefficient problem

(2 unknown + 1 fixed coefficient)

• Regular in time state function

measurements ( )

• Monotonic discrepancy decrease

Normalized cost functions cross-sections for increasing measurement datasets Reconstruction error dynamics WRT computation time

A.V. Penenko Z.S. Mukatova, A.B. Salimova Numerical study of the coefficient identification algorithm based on ensembles of adjoint problem solutions for a production-destruction model // submitted

=1, 30

meas

T N  =3, 90

meas

T N  =6, 180

meas

T N  3 10

meas

N T   

12

SLIDE 13

Data assimilation mode (2D)

(data assimilation problem = sequence of inverse problems)

=0.5 3600 T  =100

t

N 600 X Y   =100

x

N = =5

x y

  =10

t



«Inverse problem mode» (1 assimilation window) «Data assimilation mode» (2 assimilation windows) Exact source Reconstructed source

Assimilation window boundary

Penenko, A. V. Algorithms for the inverse modelling of transport and transformation of atmospheric pollutants // IOP Conference Series: Earth and Environmental Science, IOP Publishing, 2018 , 211 , 012052

Source: NO Measurements: concentration images (movies) Initial guess: zero «4DNK» algorithm

13

SLIDE 14

Sources identification with direct and indirect in situ (5 sites) measurements

NO concentrations are measured

Projection to orthogonal complement of sensitivity

• perator kernel

Reconstructed sources

Exact stationary NO source function (city traffic) O3 concentrations are measured

• V. V. Penenko A. V. Penenko, E. A. Tsvetova and A. V. Gochakov Methods for studying the sensitivity of atmospheric quality

models and inverse problems of geophysical hydrothermodynamics // Journal of Applied Mechanics and Technical Physics, 2019,

• Vol. 60, No. 2, pp. 392–399.

=4 24 3600 T   =5 10  

14

SLIDE 15

Adjoint ensemble references

15

• 1. Penenko, A.; Zubairova, U.; Mukatova, Z. & Nikolaev, S. Numerical algorithm for morphogen

synthesis region identification with indirect image-type measurement data // Journal of Bioinformatics and Computational Biology, 2019 , 17 , 1940002 doi:10.1142/s021972001940002x

• 2. V. V. Penenko A. V. Penenko, E. A. Tsvetova and A. V. Gochakov Methods for studying the

sensitivity of atmospheric quality models and inverse problems of geophysical hydrothermodynamics // Journal of Applied Mechanics and Technical Physics, 2019, Vol. 60, No. 2, pp. 392–399 doi: 10.1134/S0021894419020202

• 3. Penenko, A. V. A Newton–Kantorovich Method in Inverse Source Problems for Production-

Destruction Models with Time Series-Type Measurement Data // Numerical Analysis and Applications, 2019 , 12 , P. 51-69 doi:10.1134/s1995423919010051

• 4. Penenko, A. V. Consistent Numerical Schemes for Solving Nonlinear Inverse Source Problems

with Gradient-Type Algorithms and Newton–Kantorovich Methods // Numerical Analysis and Applications, 2018 , 11 , P.73-88 doi: 10.1134/s1995423918010081.

• 5. Penenko, A. V.; Nikolaev, S. V.; Golushko, S. K.; Romashenko, A. V. & Kirilova, I. A. Numerical

Algorithms for Diffusion Coefficient Identification in Problems of Tissue Engineering // Math. Biol. Bioinf., 2016 , 11 , 426-444 doi: 10.17537/2016.11.426 (In Russian)

• 6. Penenko, A. V. Discrete-analytic schemes for solving an inverse coefficient heat conduction

problem in a layered medium with gradient methods // Numerical Analysis and Applications, Pleiades Publishing Ltd, 2012 , 5 , 326-341 doi: 10.1134/s1995423912040052

• 7. Penenko, A. On a solution of the inverse coefficient heatconduction problem with the gradient

projection method // Siberian electronic mathematical reports, 2010, 23 , 178-198. (in Russian)

SLIDE 16

Summary

• Given the adjoint model, the sensitivity operator allow reformulating the inverse

problem stated as a PDE system to a parametric family of quasilinear operator equations

• Nonlinear ill-posed operator equation methods can be applied to the analysis

and solution of the considered inverse problems

• To solve the operator equations, the Newton-Kantorovich-type inversion

algorithm has been proposed using

• The sequential increase of the considered spectrum in TSVD
• Discrepancy principle and the iterative regularization
• Both ensemble size and its construction affects the efficiency of the inverse

problem solution (accuracy, time, local convergence)

• The algorithm was tested numerically in inverse modeling (inverse and data

assimilation, source and coefficient) problems for advection-diffusion-reaction- model.

16

SLIDE 17

Thank you for your attention!

17

Penenko A.V. Penenko V.V. Pyanova E.A. Antokhin P.N. Kolker A.B. Gochakov A.V. Mukatova Zh.S. Tsvetova E.A. Salimova A.B.

Co-authors

The work has been supported by RSF project 17-71-10184 (image-type measurements) and RFBR project 19-07-01135 (in situ measurements) .

SLIDE 18

References

18

1. Bennett, A. F. Inverse Methods in Physical Oceanography (Cambridge Monographs on Mechanics) Cambridge University Press, 1992 2. Iglesias, M. A. & Dawson, C. An iterative representer-based scheme for data inversion in reservoir modeling//Inverse Problems, IOP Publishing, 2009 , 25 , 1-34 3. Marchuk G. I., On the formulation of certain inverse problems, Dokl. Akad. Nauk SSSR, 156:3 (1964), 503–506 (In Russian). 4. Marchuk, G. I. Adjoint Equations and Analysis of Complex Systems Springer Netherlands, 1995 5. Issartel, J.-P. Rebuilding sources of linear tracers after atmospheric concentration measurements // Atmospheric Chemistry and Physics, Copernicus GmbH, 2003 , 3 , 2111-2125 6. Cheverda V.A., Kostin V.I. r-pseudoinverse for compact operators in Hilbert space: existence and stability. J. Inverse and Ill-Posed Problems. 1995. V.3. № 2. P. 131–148. doi: 10.1515/jiip.1995.3.2.131. 7. Kaltenbacher B. Some Newton-type methods for the regularization of nonlinear ill-posed problems. Inverse

• Problems. 1997. V.13. № 3. P. 729–753. doi: 10.1088/0266-5611/13/3/012.

8. Vainikko, G. M.,Veretennikov, A. Yu. Iterative procedures in ill-posed problems Moskow, Nauka, 1986 (In Russian). 9. Stockwell, W. R. Comment on “Simulation of a reacting pollutant puff using an adaptive grid algorithm” by R.K. Srivastava et al. // Journal of Geophysical Research, Wiley-Blackwell, 2002 , 107 , 4643-4650

• 10. Dimet, F.-X. L.; Souopgui, I.; Titaud, O.; Shutyaev, V. & Hussaini, M. Y. Toward the assimilation of images //

Nonlinear Processes in Geophysics, Copernicus GmbH, 2015 , 22 , 15-32

• 11. Penenko, V. V. & Tsvetova, E. A. Variational methods of constructing monotone approximations for atmospheric

chemistry models // Numerical Analysis and Applications, Pleiades Publishing Ltd, 2013 , 6 , 210-220

• 12. Hesstvedt, E.; Hov, O. & Isaksen, I. S. Quasi-steady-state approximations in air pollution modeling: Comparison of

two numerical schemes for oxidant prediction // International Journal of Chemical Kinetics, Wiley-Blackwell, 1978 , 10 , 971-994

SLIDE 19

Adjoint ensemble methods

Inverse Problem Statement (PDE) Adjoint problem Lagrange-type Identity Family of quasi-linear ill-posed operator equations Total ensemble Inverse problem solution algorithm Sensitivity function Finite ensemble Ill-posed nonlinear

• perator equation

methods # computation threads Data assimilation mode Sensitivity operator analysis

19