- The document discusses formal language theory and a measure-theoretic approach.
- It introduces regular languages and context-free languages. Regular languages have densities that are rational numbers, while it is an open problem whether all context-free languages have densities.
- The talk will cover regular measurable and non-measurable languages, closure properties of the class of regular measurable languages, and challenges.
- The document discusses formal language theory and a measure-theoretic approach.
- It introduces regular languages and context-free languages. Regular languages have densities that are rational numbers, while it is an open problem whether all context-free languages have densities.
- The talk will cover regular measurable and non-measurable languages, closure properties of the class of regular measurable languages, and challenges.
Reciprocal Theorem & Castigliano's Theorem (in Japanese) 相反定理とカスチリアの定理Kazuhiro Suga
Text book for the mechanics of materials
Reciprocal theorem & Castigliano's theorem
・Betti's & Maxwell's reciprocal theorem
・Castigliano's Theorem
・Solution of statically indeterminate beam by Castigliano's Theorem
note: Your feedback is welcome!
相反定理 & Castiglianoの定理
・Betti & Maxwellの相反定理
・Castiglianoの定理
・Castiglianoの定理による静定はりの解法
This document discusses the connections between generative adversarial networks (GANs) and energy-based models (EBMs). It shows that GAN training can be interpreted as approximating maximum likelihood training of an EBM by replacing the intractable data distribution with a generator distribution. Specifically:
1. GANs train a discriminator to estimate the energy function of an EBM, with the generator minimizing that energy of its samples.
2. EBM training can be seen as alternatively updating the generator and sampling from it, in a manner similar to contrastive divergence for EBMs.
3. This perspective unifies GANs and EBMs, and suggests ways to combine their training procedures to leverage their respective advantages
Reciprocal Theorem & Castigliano's Theorem (in Japanese) 相反定理とカスチリアの定理Kazuhiro Suga
Text book for the mechanics of materials
Reciprocal theorem & Castigliano's theorem
・Betti's & Maxwell's reciprocal theorem
・Castigliano's Theorem
・Solution of statically indeterminate beam by Castigliano's Theorem
note: Your feedback is welcome!
相反定理 & Castiglianoの定理
・Betti & Maxwellの相反定理
・Castiglianoの定理
・Castiglianoの定理による静定はりの解法
This document discusses the connections between generative adversarial networks (GANs) and energy-based models (EBMs). It shows that GAN training can be interpreted as approximating maximum likelihood training of an EBM by replacing the intractable data distribution with a generator distribution. Specifically:
1. GANs train a discriminator to estimate the energy function of an EBM, with the generator minimizing that energy of its samples.
2. EBM training can be seen as alternatively updating the generator and sampling from it, in a manner similar to contrastive divergence for EBMs.
3. This perspective unifies GANs and EBMs, and suggests ways to combine their training procedures to leverage their respective advantages
Scan Registration for Autonomous Mining Vehicles Using 3D-NDTKitsukawa Yuki
研究室のゼミの論文紹介の発表資料です。
Magnusson, M., Lilienthal, A. and Duckett, T. (2007), Scan registration for autonomous mining vehicles using 3D-NDT. J. Field Robotics, 24: 803–827. doi: 10.1002/rob.20204
The document summarizes research on simulating hydrogen dispersion using the ADVENTURE_sFlow solver. It describes modeling hydrogen dispersion as an analogy to thermal convection problems. Two models are analyzed: a hallway model and a car garage model. The hallway model analyzes hydrogen dispersion from inlet, door, and roof vents in an empty volume. The car garage model analyzes hydrogen leakage from a fuel cell car in a full-scale garage. The objective is to demonstrate the feasibility of using the ADVENTURE_sFlow solver, which uses a hierarchical domain decomposition method, to efficiently solve large-scale problems like hydrogen dispersion in engineering facilities.
31. 有限要素方程式
( ) ( ) ( )
( )
=
=+
.pgrad,A
,A,JA,pgardArot,Arot
*
hh
*
h
*
hh
*
hhh
0
ν
Ah : Finite element approximation of A by
the Nedelec elements of simplex type,
ph : Finite element approximation of p by
the conventional piecewise linear
tetrahedral elements,
Ah
*
, ph
*
: test functions.
33. 非線形反復
Newton 法
Picard の逐次近似法
( ) ( )*
hh
*
k
n
h
n
h A,J
~
Arot,Arot =+1
ν
( )
( )
∂
∂
+=
∂
∂
+ ++
*
h
n
h
n
h
n
*
hh
*
h
n
h
n
h
n
*
h
n
h
n
h
Arot,ArotA
A
A,J
~
Arot,ArotA
A
Arot,Arot
ν
ν
ν 11
37. 有限要素方程式
Ah : Finite element approximation of A by
the Nedelec elements of simplex type,
Ah
*
: test function,
( . , . ) : the complex valued L2
-inner product.
J : an excitation current density
[A/m2
] (div J = 0 in Ω)
ω : the angular frequency [rad/s],
ν : the magnetic reluctivity [m/H],
σ : the conductivity [S/m],
i : the imaginary unit.
( ) ( ) ( ).,
~
,, ***
hhhhhh AJAAiArotArot =− ωσν
40. 有限要素方程式
=−
=+
−
.0)grad,()grad,grad(
),,
~
(),grad(
),()rot,rot(
**
**
**
hhhh
hhhh
hhhh
Ai
AJA
AAiAA
φωσφφσ
φσ
ωσν
Ah : Finite element approximation of A by
the Nedelec elements of simplex type,
φh : Finite element approximation of φ by
the conventional piecewise linear
tetrahedral elements,
Ah
*
, φh
*
: test functions,
( . , . ) : the complex valued L2
-inner product.
J : an excitation current density
[A/m2
] (div J = 0 in Ω)
ω : the angular frequency [rad/s],
ν : the magnetic reluctivity [m/H],
σ : the conductivity [S/m],
i : the imaginary unit.
63. History of residual norms
History of Residual Norms (Mesh(1))
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1.00E+01
1.00E+02
1.00E+03
0 100 200 300 400 500 600 700 800 900 1000
Number of iterations on the interface
ResidualNorms
Α
Α−φ